How does one define a word that has been around for ages, and yet has no distinct meaning? Having just come out of an interview, where someone said “I want to be a part of HackerEarth because of your culture”, I found myself thinking, how oft-used (to the point of becoming banal) yet indecipherable the word culture really is. Contradictory, don’t you think?

Peter Drucker said that culture eats strategy for breakfast. I totally believe that it eats strategy for lunch and dinner too (someone said that as well I think).

The reason why culture is so hard to pin down, in my opinion, is because it’s not a specific set of processes that everyone agrees upon. Wall Street swears by its black-tie culture. Startups think it’s cool that you can use four-letter words in a conversation with your CEO and not get flak for it. Perhaps it’s not as much as what we do as what we don’t, which is the real measure of an organization’s culture.

When writing your culture handbook, the must-nots are probably more important than the must-haves. As HR professionals, it is our job to help define what the must-nots really are.

At HackerEarth, our work culture revolves around a central DON’T.

Don’t Be An Asshole.

When I first walked into HackerEarth, I was awestruck by a poster screaming “Don’t Be An Asshole”. Which, if you look closely, should be the basic tenet of every company’s culture handbook. The poster was a big part of the reason I signed up for the job.

The tenet is self-explanatory, but if asked to explain I would say it translates to don’t be that manager, that colleague, that employee, and that person who makes life hell for others at work. Period. Everything at HackerEarth stems from this basic idea of respect – for self, and for others.

Keeping respect as the central sun of HackerEarth’s galaxy helps us do a few things well:

It helps us keep our employees at the forefront of our organization.
It helps us underline what is NOT OKAY – behaviors, patterns, conflicts get called out much more easily and resolved faster.
It helps us trust our teammates and give them the freedom they need.
It helps us understand differences and find common ground.
It helps us put the team above individual egos.

Let me elaborate.

1. Keeping employees at the forefront of the organization.

Our approach is ‘inside-out’ when it comes to our people. For anything and everything we do, we start the conversation by asking what it means for our people and then move outward to answer what it means for the company. Yes, it does mean there’s a lot of care and mutual trust for our people. No, it does not mean that we don’t make difficult decisions. We make those decisions too; only we do so with care.

For my peers in HR, you need to move away from the crutches of policies, processes, and practice, and start placing that lens more on individuals. Your policies are just a guidebook. They aren’t written in stone. If you must break the rules to do the right thing, then just do it.

2. Underlining what is NOT OKAY.

We like to keep it straightforward and simple. If someone is disrespectful in their tone, or their action, we boldly call it out and tell them it’s NOT OK. In some of these conversations people stay; in others we politely, but firmly, take corrective action.

As an HR professional, you need to be proactive in constantly calling out what’s NOT OK. And that’s how the company learns to do it, too. You also need to ensure that what’s NOT OK does not change for a company’s CEO, the top leaders, your boss, your employees, or yourself.

3. Trusting our people and giving them complete freedom.

Before HackerEarth, I had never worked in a company that trusts so unconditionally. It’s commendable how we do it. We do not hide any information. We always give our people an understanding of the why behind a decision. Even if it is the decision about a pandemic-induced pay-cut or about letting someone go. If we have made a mistake, we own it. We provide every opportunity for our people to ask difficult questions. In fact, we get worried when they don’t. We are never afraid to be vulnerable in front or to show emotions, and that takes a lot of trust.

We want our people to feel as confident to say ‘NO’, as they would feel saying a ‘YES’. We’re not afraid to make mistakes. We’re famously anal about learning from those mistakes. This is part of trusting them.

Dear HR peeps, please don’t kill the human side of the job. Rather, deal with the ‘people’ side of things to make your job, and the company, better. You have plenty of time to go back to those 2 Ps (Policies, Processes) later. This also means that you need to openly show your human side, too. It’s unfortunate that the strong conditioning of the HR industry has always asked us to portray a ‘rock-solid HR’ figure. Spend time thinking about how comfortable you feel about owning your emotions, and creating a safe space for others to do so, too.

4. Understanding differences. Finding common grounds.

We let our people bring their whole selves to work. We don’t expect anyone to be under any pressure to outperform the other person. We understand that each one of us is different. Yes, we expect them to be the best version of themselves, and give their best, while respecting their innate differences.

As a startup, velocity is extremely important for us. So is listening to everyone at the table and finding what works best for an individual, the team, and the company at large. In that order. You cannot dream of the best ideas without critique. Everyone loves doers. We love and respect naysayers as well, and trust them to be our conscience keepers.

A word of caution here. Differences and diversity are beautiful, only when practiced with intent. Dear HR people, please don’t overuse the word diversity and inclusion at the drop of your hat. ‘Inclusion’ is really not for everyone. You need to be bold to allow for it. Be honest if you want similar people. That’s OK too. Be honest if you want people to say ‘yes’ all the time. But if you really want your people to say ‘NO’, then bloody well coach yourself and your leaders to listen to the ‘NO’, embrace the diverse thoughts, encourage it. I’m not championing ‘Inclusion’ for the sake of it. I’m fiercely advocating it for the merit of it – for any business and its people.

5. Putting the team above individual egos.

At HackerEarth, we pride ourselves on being the stone that cuts brilliant individuals, but it cannot stop there. They need to contribute to creating brilliant teams. We do not tolerate cut-throat behavior between individuals, ever.

Putting the team above individual egos also means allowing for and respecting divergence. Our people challenge us, all the time. And we absolutely adore the fact! We strongly believe in Team > Me and do not compromise on it, at any cost.

Dear HR people, creating a brilliant or high-performing team does not mean individuals need to be in constant competition. It means supporting them in becoming the best version of themselves. Don’t create stress in the guise of gunning for performance.

Coach your leaders to intuitively understand strengths and weaknesses. Not everyone performs at the same level.

Some parting advice from Anti-AssholeVille.

I have always believed that the role of an HR professional is to be the sole custodian of a company’s culture. In the span of my career, I have seen the conversation around work culture morph from ‘what time does an employee punch in’ to ‘what can we do to make our employees happier?’. Topics of diversity, equality, and the creation of a fair and hospitable workplace are our water-cooler gossip now.

To me, it all stems from respecting the Other. In literature and philosophy, the ‘Other’ is anyone who is not you or doesn’t follow the norms laid down by you. ‘Otherization’ is an actual verb, and something I see happening in many HR teams. We are trained to believe a good employee is one who is always hungry, acing their projects; a rabbit on a constant dopamine hunt.

For instance, when someone says “what do I need to do if I need to stay in the same job role and level for the next 3 years?”, our first instinct is to brand them as ‘unambitious’. That’s Otherization. And that’s being an Asshole.

In situations like this, I remind myself to approach with respect. The person in front of me may not have the same life goals as I do, or as I was taught that all employees should have, but it is a goal they believe in personally and I do not have the right to judge. Instead of stereotyping, I remind myself that their statement only means that this is what they want NOW. My role: to facilitate this NOW, so that I can retain a hard-working teammate for the future.

It’s NOT OK to judge, or draw people into boxes, or pitch them against each other. Or otherize them in any manner. This is where we as HR professionals need to be aware of our unconscious biases, and the assholes we can be if we let age-old dictums govern the way we create our workplaces.

So, take charge. Be open to learn and unlearn. Lead with respect so that you can create a more fair, equitable, honest, and pleasant workplace.

And never, ever, be an Asshole.

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.

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