“Love brunch? Have we got a job for you? Live for brunch, drink an Aperol Spritz®, look great, and collect a paycheck — it’s a hard job but, hey, someone’s got to do it.” This job description for Chief Brunch Officers sounds too good to be true, doesn’t it?

But it is true. In 2014, Campari launched a wonderful social media campaign for Aperol lovers to spread the happiness of the delicious Italian aperitif, which has been touted as the most fashionable drink of 2017. Sigh! Although such dream roles are few, we’d settle for good jobs that at least sound appealing.

Job descriptions are what your applicants see before all else. It can accomplish so much if done right.

And, this is especially true in case of tech jobs.

When you ask for team players, whatever do you mean?

Do you mean they shouldn’t ideally question authority? Heaven, forbid.

Or, “Works with minimal supervision” means what? That if anything goes awry, he or she gets the blame possibly? Or it could just mean what it says: your manager is too busy to keep after you and expects you do your job.

Point being made: Enough with the meaningless, ambiguous job descriptions already!

It is really up to you how you want potential hires to perceive your organization and responsibilities that go with the roles.

Like The Adler Group CEO, Lou Adler, says, “It seems obvious that if a company wants to hire people who are both competent and motivated to do the work required, they need to start by defining the work required. Yet somehow this basic concept is lost when a new job opens up. Instead of defining the job, managers focus on defining the person. The end result is not a job description at all, but a person description.”

Most JDs demand you be a team player, be innovative, take initiative, show leadership skills and a willingness to learn, perform in a fast-paced environment, etc. Which applicant is actually going to admit a lack of these skills which you can’t test until much later anyway? How are these relevant in your very first advertisement of an open position? According to a Monster survey, 57% of applicants broke into a run the minute they spotted phrases such as “ninja,” “penetrate the market,” “rockstar developer,” “hit the ground running,” and “self-starter” in the JD.

When will they stop with the ill-defined job requirements?

Courting candidates is quite the order of the day now. A time when big companies could command as they wished is no longer possible. Today, highly skilled workers are in the driver’s seat. They get to choose who they want to work for and negotiate a lot more than they did before. So, companies really can’t afford to mess up while recruiting.

After analyzing best-performing job listings for a 6-month period, Stackoverflow found that “the average apply rate for the high-performing group was 30.9%, and the average for the lower was 3.2%.” One of the main reasons for their high performance was a clear and comprehensive JD.

Seriously outdated job descriptions

You know what is really irksome? Employers using antiquated job descriptions (JDs) that should have been binned a long time ago… If you can remember your job description for your current role, then take a bow. Not many of us remember what it said; it was so lackluster and generic. Half the time, it bears no resemblance to what we are doing now.

Incomplete, vague job postings

What’s the point in advertising for abstract skills instead of telling them how they will grow or what they will own, learn, and improve? Tell them what skills are absolute must-haves. Don’t ask them if they are going to be committed. (Like you’ll believe them anyway.)

Answer these questions before keying in the JD.

What is in it for the candidate?
Why should a developer feel excited about the company/role?
Are you describing enough about what your product is trying to achieve?
How is your product impacting the globe? (Developers will find one more reason to join you if they feel their work in the company has a larger agenda.)

Confusing Ruby with a stone that’s red and shiny

Techies get it that a job role is more than a job. They get it that a job encompasses all sorts of qualities that are conventionally deemed non-job specific. However, they’d appreciate it if the recruiter knew if just knowing Java, and not Python, could jinx their chances. Talking to talent acquisition personnel who are clueless about the job requirements can’t be a whole lot of fun.

Unrealistic expectations

Companies advertise for developers who must know a string of programming languages. The tendency is to stuff the JD with many programming languages but, in general, a programmer is likely adept at not more than two or three. And what happens with the “over-optimization” of JDs is that some programmers use the languages as keywords in their resume. And eventually, this comes to bite the hiring managers when they go out to source and find that most programmers know almost half the languages on the planet. Over-optimization takes the fun away from life! Haven’t you seen this video – I miss the mob?

Ridiculous, impossible requirements

What’s really strange is when firms demand experienced professionals for jobs that are fairly new in the market. For example, if you advertise for programmers with 7 years of experience in a language that was introduced only 5 years ago, who exactly do you expect to get?

Also, before creating a JD, a recruiter should know the demographics and the sizable pool of a skill/requirement in a particular region. This sets realistic expectations and the JD will have more clarity.

Unheard of job titles

The Monster survey also found that 64% of the respondents were unlikely to apply for a job if the job title was not easy to understand. (Here’s an interesting infographic about the dilemma of job descriptions.)

According to an Australian Employment Office poll, 48% of employees say the role they were hired for isn’t the job they’re doing. For people in IT-related fields, misleading job titles are nothing new. How horrible it is when you sign on to be a project manager of an “entire group” and all you end up doing is leading a team of two (including yourself)! (It happens.) If you want a Technical Lead for Windows/Cloud, then say that and list the major skills instead of saying Technical Lead and giving a bunch of vague tasks.

How can bad job descriptions harm you?

With badly defined roles that helped you hire “talent,” you can expect to see poor productivity, higher absenteeism and turnover, and unhappy employees later on. Also, a survey showed that 78% of IT job postings are guilty of using meaningless jargon.

Rather than looking for Ivy League degrees, focus on the skills you need and tell them how they can grow with the company. It is ok to talk about the culture and the company, but not at the cost of a concise, clear, and comprehensive summary of key responsibilities. Culture and swag may win you good people, but you do need top quality talent to get the numbers going.

Sometimes, even imaginative JDs can translate into something awful or funny (if you’ve got a sense of humor). Jeff Bertolucci gave a Craigslist Wanted Ad a funny twist: Wanted: Skilled app developer who “will be paid from the profits of the app/business with a percentage stake in the company.” Translation: Until then, enjoy living out of your car. The point being that no-nonsense and clearly defined descriptions are a safer bet.

In today’s candidate-driven market, it pays to be savvy about every aspect of hiring. This makes streamlining their tech recruitment strategies imperative for hiring managers, talent acquisition officers, and recruiters. It doesn’t matter whether it’s something as high up the list as using online automated evaluation tools or crafting an attractive, realistic job description. It’s got to be well-designed if you want to have your share of great programmers in such a competitive industry.

On a side note, just what is a rockstar developer, a digital prophet, or a data science ninja?

The effect of poorly written job descriptions on tech hiring

Attracting the wrong candidates: Poorly crafted job descriptions can attract applicants who do not align with the actual requirements or expectations of the role, leading to an influx of unqualified candidates.
Missing out on high-quality candidates: Top talent may be deterred by vague, unrealistic, or overly complex job descriptions. Clear and realistic descriptions are key to attracting skilled professionals.
Inefficiency in the hiring process: When job descriptions are not clear or accurate, it leads to a longer hiring process as recruiters and hiring managers spend time sifting through unsuitable applications.
Damage to employer brand: Ambiguous or misleading job descriptions can harm a company’s reputation, as candidates may share their negative experiences with others or on social media.
Diversity issues: Overly specific or unnecessarily stringent requirements can unintentionally exclude a diverse range of candidates, reducing the inclusivity of the hiring process.
Increased turnover: If the role does not match the expectations set in the job description, new hires are more likely to become dissatisfied and leave the position, leading to higher turnover.

Tips to make your tech job descriptions better

Be specific and clear: Clearly define the role, responsibilities, and required skills. Avoid jargon and overly technical language that might be unclear to potential applicants.
Realistic requirements only: List only essential qualifications and skills. Overstating requirements can deter good candidates who might assume they’re underqualified.
Highlight growth and learning opportunities: Mention opportunities for professional development, as many candidates in tech value continuous learning and career growth.
Include information about company culture: Share insights into the company culture, values, and work environment. This helps candidates assess their cultural fit.
Be inclusive: Use inclusive language to encourage a diverse range of applicants. Avoid gender-coded words and be mindful of language that may unintentionally exclude certain groups.
Provide a clear application process: Outline the steps involved in the application process. This transparency helps set expectations for candidates.
Salary and benefits: If possible, include a salary range and a summary of benefits. This transparency can be a significant factor in attracting candidates.
Keep it concise: Avoid lengthy descriptions. A concise, well-structured job description is more appealing and easier to comprehend.
Use a friendly tone: A conversational and friendly tone can make the job description more engaging and approachable.
Get feedback: Before publishing, get feedback on the job description from current employees in similar roles to ensure it accurately reflects the position and your company culture.

PS: For more such insights on tech recruitment, we invite you to join our LinkedIn group – “Yours Truly HR”

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.

What’s wrong with today’s tech job descriptions?

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