Isabella is a recruiter at a multinational company with 1000+ employees. Her responsibility is to recruit the top talent—which she performs well by consistently looking for candidates that fit the company culture, collecting their information, reaching out to them, screening them, and finally interviewing them.

But…

She carries out the entire process manually—leading her to invest much time in the manual tasks—resulting in disengaged candidates (because who likes to wait this long?).

If you are a recruiter just like Isabella, you’ll soon find yourself burnt out and left without achieving your recruitment goals. This is where artificial intelligence has got you covered. In 2023, AI will play a major role in shaping candidate recruitment and making the lives of recruiters easy.

In this article, we’ll help you understand how artificial intelligence is used in technical recruitment.

What is artificial intelligence?

According to Techopedia, artificial intelligence is the field of study in which computerized systems can learn, solve problems and autonomously achieve goals under varying conditions. Simply put, artificial intelligence is about training the computer or the bot to do tasks that humans do—by feeding more data.

So what does artificial intelligence in technical recruitment refer to? It means a relevant technology has been used in the hiring process. There are three basic tech models artificial intelligence uses: descriptive, predictive, and prescriptive.

Descriptive: It tells us about what’s happening
Predictive: It gives us an image of the future
Prescriptive: It tells us what we should do based on the insights and findings

With this technology in the recruitment software, HR teams can focus on more strategic tasks without burning themselves out with manual efforts like candidate sourcing and outreach campaigns. Because organizations want to accelerate their talent acquisition efforts at scale, they hire recruitment agencies to find the top tech talent. But, when AI recruitment software is employed at your organization, you won’t have to rely on these recruitment agencies entirely—saving up 15-25% of employees’ first-year salary.

How can AI be used in technical recruitment?

Here are 7 ways how artificial intelligence helps improve the technical recruitment processes and set the ongoing recruiting for success.

Candidate sourcing

Recruiters spend ample time identifying top talent across hundreds of sources and platforms to find the best possible matches based on the job description—from job portals to social media profiles, they source candidates from multiple channels which is a time-consuming process.

When a recruiter sources tech candidates via LinkedIn, here’s what their process looks like:

Searches for the candidate on the LinkedIn search bar with relevant keywords
Gets a list of candidates from the received results
Scans the LinkedIn profiles of candidates who match the job requirements
Reaches out to them via Inmail or LinkedIn DMs

Improved technical recruitment with recruitment software

Recruiters have to invest time searching and scanning each profile. The process is toilsome. On average, recruiters lose 14 hours per week completing such tasks manually. Clearly, using recruitment software tools that help with candidate sourcing is a much better option. These tools look for candidates matching the job requirements and conduct outreach campaigns.

💡Pro tip: Use tools like Fetcher and Recruitee

Candidate screening

In a traditional screening process, recruiters prepare and send assessments to candidates. Once the candidate submitted the assessment for review, the recruiter checked each assignment manually.

Imagine the time a recruiter has to invest if they were to screen 100 such assessments manually. If the recruiter screened 100 candidates, they had to review all the dedicated assignments.

With AI, role-based assessments chop recruiters’ time to half. Role-specific assessments are the way AI screens the candidates. Candidates take these tests to showcase their knowledge and skills.

HackerEarth’s Assessments help you evaluate the developers’ skills with advanced coding assessments—all you have to do is upload job descriptions, create coding test interviews and find the top developers with the auto-generated leaderboard. Also, you can create the coding test interview from the list of interview questions to simplify your process.

For example, Nirvana Solutions used HackerEarth’s Assessments for technical candidate screening and reduced their cost per hire by 25%.

Why this works: Before using the automated screening method, the company relied on manual screening of applications which was a time-consuming process and led the company to exhaust its resources. Now, Nirvana’s team takes 5 minutes to create automated assessments and around 30 minutes to create the assessment from scratch. After the candidates submit the test, recruiters analyze candidate reports for each candidate’s performance—helping them screen and identify the top talent within 10 minutes.

Posting jobs

If you google “job boards to attract diverse candidates”, you’ll find endless results. Earlier, the most common were Naukri.com and Indeed. But, with time, job boards have expanded allowing organizations to distribute their job listings and attract the right tech talent.

Unfortunately, mass distribution of your job listing isn’t possible when you are a one-person army. That’s where AI tools like GoHire help recruiters and organizations automate job posting across different job boards.

Also read: A Checklist For Writing Job Postings That Actually Work

Hiring remote workers

Gone are the days when companies hired candidates from the same location. Walk-in interviews have been replaced by video interviews—which has helped companies to hire candidates while sitting in the comfort of their homes.

Sidenote: If your company follows a hybrid or remote work model, AI amplifies your recruitment efforts.

For example, after screening the candidate with automated assessments, you move to the next stage of conducting a face-to-face interview—which is conducted using a video platform like Zoom and Google Meet.

These tools miss out on features that recruitment software possesses, making AI in recruitment more prominent.

Let’s say, after screening the candidates via HackerEarth’s Assessments, you can use FaceCode to invite the candidates to schedule interviews, conduct live coding interviews with a panel of 5 interviewers, and evaluate them based on automated interview summaries with AI-based behavioral insights.

Diversity hiring

Unconscious bias is one of the staggering norms in the tech industry. Because tech is a male-dominated industry, companies see them as the right candidate compared to women. A 2022 report by Celential.ai emphasizes the gender diversity of software engineers where women represent only 21% of the workforce in software engineering.

And this bias starts right at the beginning of the technical recruitment process. Recruiters analyze the candidates based on multiple factors like location, gender, and educational qualifications on reference by the company’s employees.

But when AI comes into play in the recruitment industry, you shift towards unbiased hiring. This Twitter thread by Diversity Council Australia shares how women feel confident about applying to tech roles when companies use AI recruitment software.

Diversity hiring helps in technical recruitment

Image Source

There are two ways in which AI recruitment software removes biases:

When you use AI to send assessments to the candidates, you test them based on their skills. Once they submit the assessment, you get a report of each candidate and how they performed—this analysis helps you screen them and move to the next stage. During this process, your recruiter won’t focus on the candidate’s resume, location, or gender. They’ll *only* focus on the candidate’s skills and how they performed the assessment reducing biases.
When interviewing candidates, robots can conduct face-to-face interviews. They analyze the candidate’s response based on the conversation, facial expressions, and a mix of natural language processing and body language analysis—which helps remove prejudice

Data collection

Imagine adding recruitment software that sorts data and saves the 5 hours that you’d have spent doing the work manually. You could focus on improvising your current recruitment strategy, in these 5 hours.In a traditional setup, a recruiter would start by writing the job listing and sifting through inbound job applications to find the right fit.In the modern setup, recruiters find candidates on LinkedIn to build the database. Before building this database, they have to brainstorm and answer questions like:

How can I find the ideal candidate on LinkedIn?
How to identify and shortlist the candidate for a specific yet complex set of skills?

Once you identify the answers to these questions, you start your search, collect data and build the candidate’s database to carry out the recruitment process further. But here’s the thing: sorting through multiple data points and collecting candidates’ data is a tedious process. Employing recruitment software that collects candidates' data and analyzes it, candidate sourcing becomes much easier.AI Recruitment software like Hiretual acts as a candidate data engine for your tech recruiting requirements—it centralizes all your talent management and helps you source across 750M+ profiles and actively rediscovers lost profiles.

Candidate onboarding

From sharing the resource documents based on their role to telling them about the salary processing, health insurance, and so on—you need to exchange a lot of information with the new hire once you onboard them.No doubt you can rely on a single person assigned for helping in the onboarding process, but the time taken to onboard the new hire will take more time than expected impacting the credibility of the organization.With AI employed to onboard new employees, you can simplify the overall technical recruitment process. Seamless onboarding of new hires with chatbot

Seamless onboarding of new hires with chatbot

For instance, Unilever implemented artificial intelligence to onboard new employees. The company used Unabot, a natural language processing (NLP) bot to understand what employees need to know and fetch the information for them when asked. The AI acts as a forefront for questions employees have—from HR questions to department-specific questions like IT systems and allowances. Beyond this, it also answers questions like:

Where is the parking available?
What’s the timing for shuttle buses?
When are the annual salary reviews due to take place?

Basically, Unabot is the Alexa for Unilever—helping the company get rid of the back-and-forth of email and Slack messages to find and send resources when required.

Also read: Remote Hiring and Onboarding Tips for Technical Roles

Get ready to transform your technical recruitment process with artificial intelligence...

No doubt why recruiters like Isabella constantly ask themselves, “how to recruit tech talent?”. When you carry out each recruitment task manually, you are compressing the space for productivity and efficiency. A simple way to get out of this situation is by employing an AI-based recruitment software in your organization that:

Increases pay transparency for employees
Enhances candidate experience with pre and post-hiring communication
Provides accurate assessments of diverse candidates and identifies the most qualified candidate

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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