When you read about famous women in tech talking about their experiences, you’ll have an anecdote about how she was the only woman in the male-dominated room of tech wizards. At times ignored, women had a tough time getting their voices heard and opinions valued, and that’s putting it mildly. Many of their stories have a common thread of growing up battling stereotypes at the workplace, parental pressure at home, and a myriad unconscious biases.

Well, that’s how it was. Things must have changed. Surely. We are living in such a progressive age, for heaven’s sake.

But have they?

Reading about the recent gender discrimination fiasco at Uber, you can’t be faulted for being skeptical. Uber’s tech teams have very few women—an appalling 15.1%. And to make matters worse, the “underrepresentation” came under public scrutiny only after Susan Fowler, a reliability engineer at Uber, published a traumatizing post about sexual harassment.

It is just more proof of how many battles women have to fight, to couch in nonchalant smiles...

Statistics paint a dismal picture.

In the tech world, sexism seems to be taking much longer (than one would like) to disappear. Elevating their voices is a struggle. The awareness is there. There’s enough talk about lack of gender diversity at workplaces. But where is the conversation, huh? This post is not a feminist rant. We’ll just look at what the numbers are telling us.

Data says that women don’t really enjoy equal representation.

Source: Fortune.com (February, 2017)

In 2014, women added up to only 17% of tech workers at Google, 15% at Facebook, and 10% at Twitter according to the American Association of University Women.
In 2014, 11 global software giants published data that only 30% of the IT workforce is female.
In 2015, professional computing occupations in the US workforce held by women was 25%. This was the same number in 2008, whereas in 1991, it was 36%.
In the UK, a 2014 study showed that only 1 in 21 IT job applications were women.
In the US, 25% of the women with IT roles “feel stalled in their careers;” in India it is 45% percent and the UK it is 37%.
In the US, a 2014 study said that “unfriendly” policies, poor pay, unfair promotion, and a bro-grammer culture resulted in 45% of women leaving their tech jobs after a year.
Women hold only 26% of digital industry jobs; it is 16% in IT, and 13% in STEM.
A Stack Overflow survey says that only 8% of the software developers are women.
Women constitute just 5% of the programmers in the video game industry. However, IGDA’s survey shows an 11% increase since 2009.
Catalyst, a nonprofit organization focused on expanding opportunities for women, reported that "women in business roles within tech companies are more likely to start at the entry level compared with men.”
In Silicon Valley, women earn significantly lesser than men in similar roles.

Look at the findings of another study validating much of the stats above.A Study by the Center for Talent Innovation (U.S.): The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology (2013)

There’s a reason it’s a boy’s club, and should be.No, there really isn’t.

After surveying leaders in the IT industry, a Nominet-commissioned report, Closing the Gender Gap, revealed that the UK economy could find itself richer by £2.6 billion if it gave more IT jobs to women.
In 2015, this is what research found while analyzing code approved by GitHub: “Women’s acceptance rates dominate over men’s for every programming language in the top 10, to various degrees.” Unfortunately, this finding held true only when the women did not disclose their gender.
CodeFights found that women and men do almost equally well in coding challenges. Look at this infographic.
Stats show that in specialized coding academies, women students comprise 35%.
A McKinsey study showed that companies with over 15% of the women in top management roles had noticeably higher debt-to-equity ratios and payout ratios.
McKinsey says that the annual global GDP could go up to 26% in 2025 if women participated equally in the economy.
The Peterson Institute for International Economics surveyed 21,980 firms from 91 countries to conclude that increasing the representation of women to 30% in a company that had none to begin with could lead to a 15%-increase in revenue.

Getting back to the original question, no, the numbers don’t quite add up, at least not in Uncle Sam’s country. It is getting worse.

With 57% of the workforce being made up of women, women account for only 5% of tech leadership jobs, 19% of developers, and less than 30% of IT jobs. Microsoft reported in 2015 that women comprise 29.1 percent of its workforce, with only 16.6 percent in technical positions and 23 percent in leadership roles. Only 21% hold leadership positions in the already poor representation of women at Twitter. Only 21% of women in its 17% women workforce have managerial roles.

Except in the UK, US, and Canada, girls do better than boys in science and math at school. But somewhere along the way, this phenomenon gets buried under layers of stereotypes and circumstances, and now we have only 3 of the Fortune 500 tech companies with women as leaders.

3 out of Fortune 500 companies with women as leaders

And you thought scaling Mount Everest was tough.

In the U.S., the percentage of women majoring in computer science fell from 36% in 1985 to 18% in 2012. Girls hold themselves back for so many reasons. Self-perception is often skewed. They are even told that looking geeky with their noses in books is a major turn off for the boys.

Data shows that a whopping share of girls are interested in the problem-solving aspects and the creativity STEM offers. But they typically pick medicine or healthcare as a career choice over computers and engineering. These girls are conscious of the pervasive bias against women; they fear the isolation, sexism, and the lack of recognition they could face at the university or workplace. Some women also find programming boring. Some others believe that programming serves a male master. And stories of a viciously misogynistic Silicon Valley can’t be helping matters.

Women don’t seem to have enough role models. If they could interact or look up to more women playing starring roles in STEM related careers, it will encourage persistence. Who is going to tell them that their contribution will make a difference in the world?

However, we are in an age where fighting for their piece of the pie has been much easier for women than ever before. And, there’s mounting evidence proving how successful skilled women can be and how the world economy can only grow with more women at all levels.

Fairness is not about statistic quality —John Bercow

Fairness is about cleaning out the closet filled with centuries’ old prejudices and fears.

It is about boys at school knowing that smart girls are not intimidating or ugly; it is about girls at school knowing that the world is as much theirs; it is about parents encouraging their daughters to bravely storm male bastions; it is about skilled young women in universities believing in themselves, dreaming, and taking for granted the opportunities that will come their way; it is about women employees knowing that they can work in a safe environment unaffected by sexism, unequal recognition, and condescension; it is about not making men feel guilty for no reason; and it is about companies recognizing that gender disparity has far-reaching consequences and making a conscious effort to mitigate them.

For female programmers, HackerEarth’s International Women’s Hackathon is an opportunity to compete with other skilled developers in an algo-intensive challenge on March 8. So, get your coding hats on and get ready to save the world. (Maybe that’s a bit much. Still.)

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.

Women in tech: Do the numbers add up?

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How I used VibeCode Arena platform to build code using AI and leant how to improve it