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In Conversation: Colet Coelho, Head of Talent Acquisition, Recruit CRM

In Conversation: Colet Coelho, Head of Talent Acquisition, Recruit CRM

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Ruehie Jaiya Karri
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March 24, 2022
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3 min read
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Hire IQ by HackerEarth is a new initiative in which we speak with recruiters, talent acquisition managers, and hiring managers from across the globe, and ask them pertinent questions on the issues that ail the tech recruiting world.

Next up in this edition is Colet Coelho, Head of Talent Acquisition, at Recruit CRM. Being Women’s History Month, we wanted to understand the diversity mandates at Recruit CRM and more importantly, as a woman in tech, what would Colet like to change for welcoming more of such awesome women into the tech recruiting space.

Settle in, and let’s get to it!

P.S. If you missed the first edition of HireIQ where we sit down with Charles Rue from IHS Markit, you can read it here 🙂

HackerEarth: A lot of recruiting jargon has made headlines in the last two years. Candidate experience, remote hiring, employee burnout, and of course the ‘Great Resignation’. If you had to pick one jargon/phrase to attach to the future of ATS platforms, what would it be and why?

Colet: If I had to pick a jargon out of the mentioned, I would pick two—candidate experience and remote hiring. The candidate experience we design reveals a great deal about who we are as an employer. Prospective workers will judge our company based on their experience with the recruiting process, and a negative applicant experience will discourage future job seekers from applying.

Although, providing an excellent candidate experience can be a problematic aspect of the remote hiring process. Enhancing the overall candidate experience in remote recruitment is a vital function of an Applicant Tracking System. ATS platforms automate hiring while streamlining this entire process.

Recommended read – Remote Work And Recruitment: An ATS Story

HackerEarth: How have your internal hiring policies changed in the last two years? Since DEI has been a priority in the tech world, have you initiated any new processes for improving inclusion at your workplace?

Colet: Our hiring policies have been pretty consistent. Since we have always been a remote functioning organization, the last two years haven’t affected our recruitment methods too much.

Our organization has a fair distribution of employees from different backgrounds, ethnicities, and locations spread across the globe. We host regular meetings with all our employees where everyone is heard and allowed to present their views forward.

To create an inclusive culture, starting at the very beginning is critical. We preach, advocate, and encourage inclusivity as an essential component of our organizational principles. We’ve started sharing it on our social media, websites, and in interviews so that any potential employee is aware of our inclusion goals.

We have also begun to streamline the recruiting process by enabling candidates from various community outreach initiatives, job fairs, and hiring consultants to participate. This guarantees that we have a varied range of abilities.

Inclusion at the workplace is pointless if people are not valued for who they are.

The pronouns that a person prefers are entirely up to them. We will begin to include a section on the pronouns employees prefer on their identity cards. In addition, we will guarantee that all of our job descriptions include gender-neutral language.

HackerEarth: As a woman in the tech recruiting space, what are some of the changes you would like to see in how companies attract talented women? How about expanding the conversation to include the non-binary community, and if yes, then how can recruiters begin to do that?

Colet: Flexibility is one of the top perks a firm can offer an employee, not just to women but also men. As a woman in the recruitment sphere, I’d like to see companies offer women more flexibility regarding where, when, and how they work.

Flexibility for women in tech

To attract non-binary candidates, recruiters can start by allowing a range of pronouns in different areas. Leaving gender boxes unfilled or providing the opportunity to add additional gender or pronoun categories in both paper and online HR forms/platforms will encourage non-binary employees to apply as well as feel like they belong in the workplace.

HackerEarth: Data has become an important tool for recruiters today. In your opinion, what do you think are the three most important markers/data points that recruiters should be looking at when hiring? Additionally, do you think there is a data point that recruiters are overlooking?

Colet: The three most important data points that recruiters should always consider are quality of hire, cost-per-hire, and time-to-hire.

While assessing hiring quality might be subjective, it is probably one of the most critical criteria to monitor. Poor performance can indicate that you have an individual performing the wrong job, regardless of how quickly you fill a role or how much you lower the hire cost.

The cost per hire is simply the money spent on recruiting in a given year divided by the total number of hires made. The recruiting costs vary for every organization, so it’s a good idea to benchmark the typical expenditures for various jobs in your firm. The time-to-hire metric measures how fast an applicant progresses through the various phases of the recruiting process.

The total amount spent on recruitment in a given year divided by the number of recruits is the cost per hire. Again, the recruiting expenses differ from one organization to the next. Therefore, it’s good to benchmark the average expenditures for various roles inside your company.

Data points to improve your tech hiring process

A critical marker that recruiters occasionally tend to neglect is the source of hiring.

Knowing where your best candidates and applicants are coming from is quite helpful, especially when it comes to recruitment marketing. With this indicator, you can discover those sources and channels that bring in the most qualified candidates for your available positions.

Recommended read: The Great Resignation In The Tech Industry – How To Prevent it

HackerEarth: One of the questions we love asking tech recruiters is – when it comes to skills versus experience, what would you choose and why? What are some of your trusted markers for a skill that you would use to gauge a developer’s competency when hiring?

Colet: While I’m constantly emphasizing soft and hard skills as a recruiter, I can’t ignore the importance of work experience to evaluate a candidate. Therefore, when I search for an ideal candidate, I am looking for a combination of the right personality, soft skills, technical or hard skills, and practical industry experience.

Although, a lack of corporate experience is not an indicator of poor potential, especially when hiring youth. The ability to work in a team structure, make decisions and solve problems, communicate verbally with people inside and outside an organization, and plan, organize and prioritize work are skills I focus on evaluating when hiring.

My usual evaluation method is looking at past projects and giving real-time assignments to measure a developer’s expertise when recruiting.

HackerEarth: Let’s talk a bit about workplace culture 🙂 In the era of hybrid, how do you suggest companies can keep up employee morale and boost engagement?

Colet: I truly believe that open communication and prioritizing employee well-being are the way to maintain morale at work. Honest communication facilitates trust, and employees who have faith in their supervisors to act in their best interests are less stressed during times of transition and uncertainty. Considering employee mental health is also a critical factor in ensuring high morale, especially concerning feelings of isolation and the rising risk of burnout.

Ultimately, leaders cannot just guess or intuit what would make staff feel the most upbeat and engaged. They are the only ones who can tell you what works for them.

So I suggest that companies solicit constant input from employees through employee engagement surveys to gather personal knowledge and then develop a curated strategy that tailors efforts to people’s preferences and requirements.

HackerEarth: Recruit CRM also helps companies with sourcing. What are some non-traditional modes of sourcing you have seen your clients use in the recent past that you think have great potential? Alternatively, do you think there are untapped platforms that tech recruiters can use to their advantage?

Colet: I’ve seen clients use quite a few non-traditional methods of sourcing that have turned out to be quite the successes. For example, sourcing through social media and Boolean searches. Social networking has evolved into one of the most effective tools for hiring today. LinkedIn, Github for developers, and Behance for creatives are the most well-known professional platforms.

However, popular social media platforms such as Facebook, Twitter, Instagram, and Snapchat are valuable tools. I feel like Quora and YouTube are two such platforms that haven’t been tapped by recruiters yet but can prove to be of great help in finding potential employees.

Recommended read:Boolean Search Strings – 5 Essential Tips For Recruiters

HackerEarth: And lastly—a piece of advice for recruiters around the globe to navigate the pandemic-induced ups and downs of the recruiting business.

Colet: The last two years have been tough on recruiters. The pandemic and its consequences decimated some talent acquisition teams, piled additional pressures on others, and proved to be a historic change agent, as virtually recruiting and onboarding a remote workforce became the norm for many.

Since virtual hiring is here to stay, I would advise recruiters to focus on making virtual recruitment as streamlined and fine-tuned as possible using an ATS. In addition, a common challenge resulting from the pandemic is the difficulty in filling job openings.

The difficulty in getting applications is an excellent opportunity for some clever employer branding.

Take advantage of this chance to establish your employer brand and set your organization out of competition. Address the main worries of your present workers and future applicants by assuring them that your firm is solid and helpful.

Colet Coelho

About Colet Coelho:

Based out of Mumbai, India, Colet heads the Talent Acquisition team at Recruit CRM, aiming to bring the best talent onboard and scale the current team of 50 to over 150 in the next 2 years. Here is her LinkedIn.

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Author
Ruehie Jaiya Karri
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March 24, 2022
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3 min read
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How I used VibeCode Arena platform to build code using AI and leant how to improve it

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

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."

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.

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

The Mobile Dev Hiring Landscape Just Changed

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:

Article content

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.

Article content

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.

Vibe Coding: Shaping the Future of Software

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

Vibe Coding Difference

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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