Tech recruiting can get a bit dull at times. That’s when I turn to my tried-and-tested source of humor - Dilbert. A laugh-and-a-half helps me remember why I love doing this job - because it matters! I enjoy talking to recruiters and hiring managers and writing about real-life tech hiring problems and their solutions. Here's a recent Dilbert strip I chanced across while working on this piece.

Tech Assessments - Dilbert Cartoon

So, let’s talk about the problem at hand - Assessments - and the many ways in which recruiters can get it wrong. (Not intentionally, of course. No offense meant, amigos! I’ll leave that to Dilbert and his ilk :))

The Unintentional Mistakes Recruiters Make With Tech Assessments

Finding good tech talent is every recruiter’s dream. Sometimes, it can feel like you’re doing everything right; and yet the results are not coming in. We have asked this question to many recruiter friends and they say that many times, the problem lies in the assessment phase of the hiring funnel.Tech assessments sound simple, right? Send a developer a problem statement, ask them to hand in code submissions, review the code and voila! You have a match. In reality, quite a few things can go awry with your tech assessments. Let’s take a look:

1. Long Tech Assessments = Time Sink

Tech hiring is known to be a notoriously long process. However, before you send in another tech assessment that requires days to complete, ask yourself if that’s really necessary. The longer a take-home assessment requires to finish, the less likely it is that the candidate will complete it.Assume that candidates interested in your role are also talking to other companies, many of which will require them to complete take-home projects. As such their projects will stack up, and if your candidates are also working full-time jobs, they simply won't have enough time to complete long projects for free. Additionally, good engineers know how much their time is worth—asking for hours of free code is going to lead experienced engineers to drop off.

So, what can you do to reduce drop offs? Respect your candidate’s time. Keep your assessments short and timely as much as possible. If a certain role requires a long take-home project then consider making it a paid project to retain interest, and to not let the developer feel like their time has been taken for granted.

2. Take Home Assessments + Onsite = Too Many Expectations!

Many companies combine take-home assessments with an onsite test as well. For engineering candidates, this can turn out to be a severely demoralizing experience. Imagine spending hours on a take-home to showcase their best efforts, only to be called into an onsite interview where the manager clearly has no clue about your skills because they never looked at your submission.Recruiters in today’s day and age cannot expect candidates to be at their beck and call. If a take-home needs to be coupled with an onsite assessment, then begin by clearly defining these expectations during the initial screening round. If an engineer is walking through your office doors (virtual or otherwise) for an onsite project, they respect the time they put into the project.

How can you make the onsite experience better for your candidates? First up, understand if your candidate is ready for this. With the pandemic, many of us have become caregivers for our families, and it may not be possible for every candidate to dedicate extra time for both a take-home and an onsite test. If they do agree to an onsite, use the opportunity wisely to see how they integrate with the team. Talk through their code-writing process with them, understand their decision-making process, and become privy to how they think about software.
Don’t, and I repeat - don’t, make it just another hoop for them to jump through.

3. Picking Resumes Over Assessments for Lateral Hires

One of the biggest mistakes many recruiters and hiring managers make when selecting lateral hires is the decision to skip assessments for experienced developers. Sometimes this decision can also be taken in order to prevent any discord - experienced developers have been known to take offense at being asked to ‘prove’ their skill.Allow me to present an analogy - the recipe for baking cake is the same, innit, but not every chef cooks up the exact same dish. Oven temperatures differ. Techniques change. Even the minutest of alterations in the recipe can provide for amazing differences.

So while it’s true that experienced devs come with a proven skill-set, it does not automatically make them the right fit for your team. Technical assessments are a proven way of judging for this ‘team fit’, and you should not gloss over it just because someone has an impressive resume.

What is the secret to using technical assessments for better lateral hiring? When hiring experienced developers you are not looking at problem-solving ability, or a skill fit. Your candidate already has that. What you need to check from a hiring perspective, is what it would be like if the candidate worked on your production code in real time. The closer the prospect’s project is to the real work you and your team does, the better the signal that they are the right choice for your team.

4. Using Manual Reviews Without Proper Benchmarks

There’s proven data to show that top talent is ‘off the market’ within 10 days of them becoming ‘available’. There is a very small window to attract the best of the best, and the scope for errors is nil.Now, imagine you’re a recruiter trying to tap into this talent pool. You spend a couple of days talking to and screening candidates. Then you send across a 2-day project to a candidate. On submission, you can email it across to your hiring manager for review. The manual review takes another two days. By this time, a week has already passed and you just have 3 days to schedule interviews, and make an offer. Another company that uses automated assessments gets the edge over you because they used a much more efficient method of assessing and evaluating candidates. Developer Hiring Statistics - hackerEarth

Developer Hiring Statistics - hackerEarth

Automation ensures speed, accuracy, and an objective bias-free evaluation process where every developer is assessed according to standardized benchmarks. Apart from efficiency, automated assessments are also beneficial in removing errors during manual reviews. In short, by using automated assessments over manual reviews you are creating an error-free process where only the top skills filter through.

Creating The Perfect Tech Assessment

We’ve spoken to many tech recruiters over the years to understand what makes a good coding assessment. Here’s what we gathered:

A good coding assessment is true to the role at hand, and is customized to assess the exact skills required for the role. You cannot hire exceptional people with generic assessments.
It needs to be standardized. So, if there are 20 applicants for a given role, all 20 should be asked to take the exact same test, so that the results can be benchmarked.
A good coding assessment should provide a more accurate work sample than whiteboard interviews or timed challenges can ever do.
With a take-home coding assessment, the key is to allow the candidate to out their best foot forward. The assumption is that by taking the test in the comfort of their homes at their own convenience, they will be under less pressure and will perform better. So, there should not be an element of unwanted stress by making the assessment more complex than is necessary.
At all times, it is imperative to RESPECT the candidate, their time, and their skills. If you’re asking them to code for 10 days for free, that’s not the hallmark of a good employer.
Using automated assessment tools and question templates can go a long way in helping you make your assessment process error-free. At the end of it all, do remember that while there may not be a one-size-fits all solution, there are some tenets that will remain permanent.

Don’t use the take-home assessment as ‘just another step’ in the hiring process. Use it wisely, so you can save time in the interview process, and not lose out on hiring the top talent due to inefficient processes. A well-crafted technical assessment can help you better evaluate your talent pool, and take some of the stress off of your hiring managers -- but it works well only when you remember to respect and stay invested in your candidates.

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.

Top Developers Point Out 4 Mistakes With Tech Hiring Assessments

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