With advanced technology at our fingertips, isn’t it only logical that this technology is used for HR automation so that some of these mundane and transactional tasks to a bot, freeing up the HR personnel to focus on more value-added tasks?

Technology has made huge inroads into our professional and personal lives.

And it is no wonder then that a department that hustles several transactional tasks, which are routine yet vital to the smooth running of the organization, is undergoing a major transformation with technology at the helm.

Right from recruiting skilled talent (quite a long-drawn-out process that one!), onboarding them, entering employee data into the payroll system and updating it regularly. As part of this transformation, many companies are also adopting online billing servicesto streamline their financial operations. These services help automate invoicing, payments, and expense tracking, reducing the administrative burden on HR and finance teams. Utilizing anaccurate paystub makerfurther enhances efficiency by ensuring precise and compliant payroll documentation.

Granting leaves, handling shifts, assisting employees with any documents they might need.

Preparing tons of recruiting reports to promote companies at job events, organize external/internal training for employees, gathering feedback from employees, managing internal communication with employees, the operational and functional tasks that come under the purview of Human Resources are rigorous.

From the above list of exhaustive tasks that every HR department is laden with, most rely very heavily on documentation and manual processing.

(Also read – How to begin your digital recruitment strategy)

5 types of HR automation tools

Pre-Recruitment HR automation

In this article, we will focus on one of HR’s most crucial functions — recruitment. Traditionally, this has been an area that demanded a considerable chunk of the HR department’s time and efforts.

A majority of the Millenials are familiar with how recruitment was done before the advent of technology.

It was not very long ago that most households with job aspirants were witness to a morning routine of the aforementioned character hunched over the morning’s newspaper, armed with a pen in hand circling a few of the many job postings in the classified section while sipping a cup of piping hot coffee.

Once the job postings were selected, the next task was to send printed copies of their resumes in neatly addressed envelopes to the addresses mentioned in the postings or at times, personally go deliver it at the organization.

The HR department of the organization, upon receiving this application would go through it and on finding it relevant, would forward it to the manager, who would take a call on whether to proceed with the particular applicant.

While this picture looks simple enough, in reality, there is a stark difference.

For instance, it was not just one or two such applications that the HR department would have to go through, but several hundred for each role advertised.

Manually going through each and every one of these was hardly time efficient if not impractical. All that changed with the advent of technology.

While newspapers still post job ads, most of the advertising happens online on career building network sites, forums, or other forms of social media.

Job aspirants, too, are no longer hunched over their daily newspapers, but are constantly checking for notifications about their job applications ((among other things, of course!) on their phones or laptop (with the hunching intact).
Post-recruitment HR automation

Recruiters can now sift qualified candidate resumes and discover some gems in the process from the many networking sites and other sources.

The ease of applying for a job that the internet has facilitated comes with another daunting challenge for the recruiter.

Selecting the right candidate from a sea of qualified and skilled applicants would be akin to fishing with a handy little fishing rod (to take the analogy further)!

Let’s take a look at some specific areas of recruitment where automation can assist the HR to make the entire process easier, faster, and better.
Resume screening automation

The task of manually filtering through scores of resumes to find the ideal candidate is a daunting one, with a recruiter spending on average 23 hours screening resumes for a single hire!

Around 52 percent of talent acquisition leaders consider resume screening to be the hardest and most time-consuming part of recruitment.

It is for this reason that recruiters are turning to technology that goes a long way to alleviate this burden. With the help of data-driven automation software and applicant tracking systems, companies are able to function efficiently even when faced with a barrage of resumes.

This intelligent software empowers the HR team to screen a select few candidates who meet their requirements.

However, many industry experts such as Lou Adler, CEO of Search and Training firm The Adler Group, believe that today’s prevalent technology is ‘still about weeding out the weak rather than finding the best’.

Volume recruiters would beg to disagree since they consider these software invaluable tools that enable them to focus on more value-adding tasks.
Talent assessment automation

Manually keeping an applicant updated throughout the recruitment process is yet another one of those crucial yet challenging tasks that the HR team struggles with.

Considering the importance job seekers place on being regularly updated (67 percent reported to have a positive impression of the company that gave them consistent updates according to data from CareerBuilder).

What better way to communicate with the applicant in a consistent manner than to engage them in some important pre-qualification information pertaining to their location, compensation, intent, and special skills if any?

Not only would this be helpful in understanding the candidate better, but it would also engage the candidate during the recruitment process.

An intelligent chatbot can handle the task of asking repetitive questions to candidates, thus engaging the candidate as well as enabling the recruiter to focus on other tasks.

(Also read – 5 reasons you should use talent assessment software)
Interview process automation

Interviewing a candidate to get the best out of him/her requires skill, tact, and intuition among other qualities, which an interviewer may not always be blessed with.

How many times have we sat across an interviewer, wiping sticky sweat across our brows and upper lip (ughh…), under his agonizing gaze, trying to form coherent sentences that would make even our weaknesses look like our strengths!

This is not to say that all interviews are disasters, but with human biases aplenty, understanding a candidate’s strengths and weaknesses, his/her passion is not an easy task.

Automation can help recruiters immensely in this respect — recruitment interviews conducted in collaboration with digitized interview technology equipped to assess factors such as their word choices, speech patterns, and even facial expressions can help decide how well a candidate will fit the role.

Apart from these benefits, automated recruitment software also empowers candidates to schedule interviews as per their convenience at home or a venue of their choice.

(Also read- How to improve your recruitment process using skill assessment)

HR automation software, a necessity?

As discussed above, automation of certain processes pertaining to resume screening, pre-qualification, and interviews make the entire process efficient and provide a positive experience to the candidate as well.

According to Ideal, a virtual recruitment assistant, a great example of HR automation aiding in recruitment is the iconic British clothing retailer M&S, who created over 35,000 job vacancies by automating the entire process, manually supported only by two human touch points making it an extremely efficient process and garnering a whopping 98% rating from its users!

Apart from providing a positive experience to the candidate, technology is helping recruiters with their administrative burden drastically.

With an ever-increasing hiring volume (56 percent according to a survey of talent acquisition leaders) but with 66 percent of recruitment teams remaining the same size, automation can help ease the burden on HR personnel.

(Also read – What are common mistakes in tech hiring)

Recent studies have revealed that some of the best-in-class recruitment practices involve reaching out to candidates long before they apply (or even sense the need for a job change) for the position.

More and more organizations are opting to engage and educate potential talent about the opportunities they offer, a paradigm shift from a traditional recruiter to a strategic one that follows proactive recruiting.

According to Aptitude Research partners, a Boston-based analyst, and advisory firm, this strategic shift is also resulting in close to 70 percent of enterprise companies investing in these functions.

This transformation of HR from an administrative facilitator to a strategic partner has been made possible thanks to a technological collaboration.

Boon for some, bane for others, it is imperative to understand that automation, when strategically applied to one’s recruitment process, can yield significant results.

Automate your hiring process using HackerEarth’s Talent Assessment Software

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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.
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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.

5 types of HR automation tools for effective hiring process

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