Estimated read time: 8 minutes
If you lead a technical hiring pipeline, your ATS is no longer the bottleneck you can ignore. Per the 2023 Ashby Talent Trends Report, applications per hire have roughly tripled, and keyword-matching ATS tools cannot keep pace with that volume. AI interviewer platforms — software that conducts structured, two-way candidate conversations using voice or video avatars and applies a consistent rubric to every response — are increasingly being used to supplement or replace ATS resume screening as the first filter in technical hiring. For recruiters and talent acquisition leaders, the practical question is which parts of screening to hand off to an AI interviewer and which to keep human.
Talent acquisition teams face a measurable volume problem. The Ashby report cited above also documents a significant rise in interviews per hire year-over-year; specific percentage changes vary by role and segment within the underlying dataset, but the trend line is consistent: recruiters spend more time filtering unqualified candidates than engaging promising ones.
Credit - Ashby Talent Trends Report (2023)
For technical roles, the burden compounds. Hiring a developer or engineer typically requires more interview hours than a comparable non-technical role, though the exact gap varies by company, level, and source. The cost is not just financial. It is the opportunity cost of delayed projects, engineer interview load, and a recruiting process that cannot scale.
Cost-per-hire data from the SHRM 2022 Talent Access Report puts the average cost per hire at roughly $4,700, with senior and executive-level technical hires often running several times higher. These figures do not account for the hidden costs: recruiter overtime, engineering capacity consumed by interviews, and productivity loss when roles stay open for months.
Traditional ATS-led hiring carries deeper costs that rarely appear on spreadsheets — and most of them land directly on the recruiter's desk.
Recruitment capacity is the first casualty. When recruiters spend the majority of their week on administrative tasks and initial screenings — a pattern reported across recruiter productivity surveys, including Ashby's — they have little time for the work that builds their credibility with hiring managers: sourcing passive talent, calibrating on role requirements, and managing candidate relationships through to offer.
Inconsistent evaluation is the second. Different interviewers ask different questions, evaluate against different standards, and bring different energy levels depending on the day. One candidate may face a rigorous technical grilling while another moves through with surface-level questions. For a recruiter, this inconsistency erodes trust with the hiring manager — every debrief becomes a negotiation over whether the signal is real or an artifact of who ran the screen.
Human bias is a related vulnerability. Research summarized by SHRM finds that unstructured interviews are vulnerable to unconscious bias — affecting decisions based on candidates' names, educational backgrounds, or even interview time slots. These biases also create legal exposure under frameworks such as NYC Local Law 144, EEOC guidance on algorithmic hiring tools, and the EU AI Act's high-risk classification for hiring systems.
Candidate experience is the final cost. According to CareerPlug's 2024 Candidate Experience Report, 52% of job seekers said they have declined a job offer because of a poor hiring experience. When candidates wait weeks for feedback or endure disorganized interviews, they share those experiences, which erodes employer brand.
High-performing technical hiring teams share three operational traits: objective evaluation, consistent methodology, and efficient throughput. Each can be tied to a specific recruiter workflow change.
The three pillars of modern talent acquisition
Objective screening means every candidate is scored against the same rubric, independent of the interviewer's mood or the candidate's name. Specifically: define a rubric tied to the role's competencies, score against that rubric, and require evaluators to cite evidence from the response. Companies that adopt rubric-based screening report more comparable data across candidates and reduced reliance on gut-feel decisions. For a deeper look at rubric design, see our guide to structured technical interviews.
Consistent methodology means the same questions, the same rubric, and the same scoring pass for every candidate, whether they apply at 9 AM Monday or 11 PM Friday. This consistency produces data that can be benchmarked over time, so recruiters can refine criteria based on actual hire outcomes.
Efficient processes mean screening hundreds of candidates without proportionally adding recruiters or engineering interview load. Specifically, recruiters delegate first-round structured screens to an AI interviewer and reserve their own time for offer conversations, calibration, and pipeline strategy.
Large enterprises historically built this through standardized interview training, structured scorecards, and dedicated recruiting operations teams. AI interviewer tooling now puts a similar standard within reach of smaller teams.
An AI interviewer addresses volume directly: structured first-round conversations run in parallel, on candidate time, with scorecards delivered to recruiters rather than added to their calendars. Some HR teams report measurable reductions in time-to-fill after introducing AI-driven screening, though the magnitude of reduction varies by organization, role, and how the tool is integrated.
The bias-reduction case is more nuanced than vendor marketing suggests. Structured, rubric-driven evaluation is more consistent across candidates than human-led screens, because the same questions and scoring criteria apply to everyone. That consistency reduces some forms of interviewer variability, but AI systems can also encode bias from their training data, which is why frameworks such as NYC Local Law 144 require bias audits of automated employment decision tools.
For recruiters, an AI interviewer shifts the role from administrative coordinator to talent advisor. Instead of running repetitive first-round screens, recruiters can spend that time on candidate engagement, offer negotiation, and pipeline development. Practically, this means recruiters can review structured scorecards and recordings rather than conducting every introductory call themselves. For more on the recruiter productivity shift, see our post on recruiter workflows in technical hiring.
AI interviewers are not the right fit for every role or context. Senior leadership hires, highly creative positions, and roles where cultural judgment is the primary signal still benefit from human-led conversations. Candidates with low-bandwidth internet connections, older hardware, or accessibility needs can be disadvantaged by video-based AI assessment, which is a reason to offer alternative formats. Jurisdictions including New York City and several U.S. states require bias audits and candidate notification for automated hiring tools; the EU AI Act classifies hiring systems as high-risk and imposes additional transparency obligations. Any AI interviewer deployment should account for these limits rather than treat the tool as universal.
HackerEarth offers two products that together cover the work an ATS resume scan used to do: OnScreen, an always-on AI interview platform using lifelike video avatars for role-calibrated conversations with candidates, and Skill Assessments, a configurable technical assessment product used by 500+ global enterprises for coding evaluation. Together, they map directly to the three pillars defined above.
OnScreen addresses consistency through a deterministic rubric applied identically to every candidate, so evaluation is more consistent than human-led screens and does not vary by interviewer mood or fatigue — a human variable that structured rubrics eliminate. It addresses objectivity through KYC-grade identity verification that confirms the person interviewing is the person being evaluated — a control point that ATS resume screening has never offered. And it addresses efficiency through role-calibrated conversations that adapt to candidate responses, run on candidate time, and return a scorecard a recruiter can review. The underlying evaluation model is configured around the role's rubric and competencies rather than acting as a general-purpose chatbot; buyers should confirm training-data and audit specifics with HackerEarth directly. Skill Assessments cover the coding evaluation layer, with a library of role-mapped questions across 40+ programming languages and a browser-based code-execution environment. HackerEarth's customer stories include examples of teams using these products in technical screening pipelines.
A note on what is and is not claimed: specific IDE integrations, plagiarism-detection capabilities, and weekly time-savings figures depend on plan and configuration, and prospective buyers should confirm scope with HackerEarth directly rather than rely on aggregated marketing numbers.
If you are evaluating a first-round screening change, a practical starting point is to pilot a structured AI interviewer alongside your current process for 60–90 days on a single role family, then compare scorecard data to hire outcomes before broader rollout.
See it in your workflow: Request an OnScreen demo to walk through the structured interview flow, identity verification, and scorecard review on a role of your choice.
What is an AI interviewer — and what is it not? An AI interviewer is a first-round structured screen, not a hiring decision-maker. It is also not a replacement for hiring-manager judgment on scope, level, or team fit. The definition breaks down in practice when teams use AI interview scores as a sole pass/fail gate rather than one signal in a scorecard reviewed by a recruiter and hiring manager.
Does AI interviewing reduce bias? AI interviewing can reduce some forms of interviewer variability because the same questions and rubric apply to every candidate. It does not eliminate bias: AI systems can encode bias from training data, which is why jurisdictions such as New York City require bias audits of automated employment decision tools under Local Law 144.
How does an AI interview agent work? An AI interview agent presents questions to a candidate, captures responses (text, voice, or video), evaluates them against a predefined rubric, and returns a structured score. Platforms such as HackerEarth's OnScreen add identity verification and role-calibrated conversations that adapt to candidate responses through a lifelike video avatar.
Does replacing ATS resume screening mean removing resume review entirely? No. Resumes still matter for verifying credentials, employment history, and clearances that an interview cannot surface in a short window. The shift is sequencing: skills demonstration moves earlier in the funnel (via a structured AI interview or coding exercise), and resume review becomes a supporting check rather than the primary filter.
Are AI interviewers legal to use in hiring? In most jurisdictions, yes, with conditions. NYC Local Law 144 requires bias audits and candidate notification. The EU AI Act classifies hiring AI as high-risk and imposes transparency requirements. EEOC guidance applies to algorithmic hiring tools in the U.S. Confirm requirements in each jurisdiction where you hire.
When should you not use an AI interviewer? Senior leadership roles, highly creative positions, and contexts where candidate accessibility or connectivity is a concern are usually better served by human-led or hybrid formats.
Hiring has always been a challenge, but in today’s competitive market, it feels tougher than ever. The best candidates often juggle multiple offers, and companies that move too slowly lose out. On top of that, recruiters spend hours on repetitive work — scanning resumes, coordinating interviews, chasing paperwork.
This is where recruiting automation steps in. What was once considered a niche HR tool has now become a business essential. Done right, automation doesn’t replace human recruiters. Instead, it makes them more effective by freeing them from manual tasks so they can focus on building relationships and making smarter hiring decisions.
At its core, recruiting automation uses technology to handle tasks that recruiters traditionally did by hand. Think of activities like sourcing candidates, screening resumes, scheduling interviews, sending reminders, or even creating onboarding documents.
This idea is part of a bigger trend called hyperautomation, where multiple technologies like AI, machine learning, and robotic process automation come together to streamline entire workflows. In recruiting, it means integrating tools so that everything from finding talent to managing employee records connects smoothly. The real power lies in building an end-to-end system where data flows seamlessly across HR and business platforms. This way, hiring isn’t just a standalone process but part of the organization’s larger growth strategy.
The business case for AI recruiting automation isn’t just about saving effort — it’s about measurable returns.
Speed is critical. The average time-to-hire in 2025 is 36 days, which leaves plenty of room for improvement. Companies like United HR Solutions showed how AI platforms reduced time-to-hire by 45% and time-to-fill by 47%. In many cases, automation slashes hiring time by 30–50%.
When candidates receive faster responses and quick offers, companies avoid losing them to competitors. This also reduces the cost of vacant positions and boosts candidate satisfaction.
Hiring is expensive. Globally, the average cost per hire is around $4,683 when factoring in ads, recruiter hours, and agency fees. Manual scheduling alone can eat up five hours per candidate.
Automation cuts these costs significantly. Studies show administrative overhead can drop by up to 80%. Some reports estimate that AI recruiters can save as much as $16,000 per hire, thanks to faster shortlisting and reduced manual screening.
Another advantage: while manual costs rise with the number of hires, automated systems stay stable, making them ideal for fast-growing companies.
Automation also raises the bar on candidate quality. AI tools focus on skills and experience, reducing unconscious bias and creating a fairer process. Resume-screening accuracy can reach 85–95%, far higher than manual reviews.
Case studies show a 40% boost in candidate quality scores and a 36% rise in sourcing quality after automation. Hiring better-fit employees lowers turnover, saving money and building stronger teams.
Today’s candidates expect fast, transparent communication. Automation ensures they get it. Chatbots answer questions 24/7, automated emails provide updates, and scheduling tools let candidates book interviews at their convenience.
Companies using these tools report a 49% drop in candidate drop-off and a 44% increase in satisfaction. For example, the American Heart Association doubled its sourcing activity and boosted recruiter engagement by 50% after cutting administrative work with automation.
Automation isn’t one-size-fits-all. Some platforms are designed for specific industries. HackerEarth, for instance, specializes in tech hiring.
Adopting recruiting automation requires more than just buying software. Success depends on strategy and people.
Pick tools that are scalable, easy to use, and able to integrate with your HR stack.
An Applicant Tracking System (ATS) often serves as the hub. The best setups integrate with CRMs, payroll, and learning platforms. Tools like Zapier help connect different apps into a unified workflow.
Resistance is common. Recruiters may worry about losing relevance or struggling with new tools. The solution is open communication and involvement. Bringing teams into the process early can increase adoption success rates. Hands-on training and continuous learning opportunities ease fears and ensure recruiters can fully use the new system.
Contrary to fears, AI will not replace recruiters. Instead, it will reshape their role. The best outcomes will come from a human-AI hybrid model. Recruiters will be able to focus more on relationship-building, candidate engagement, and employer branding, while automation provides efficiency and insights. Those who embrace this partnership will be the most successful in the talent market of the future.
Recruiting automation is no longer optional. It speeds up hiring, cuts costs, improves candidate quality, and enhances the overall experience. It’s about creating a partnership where automation handles the repetitive work, and recruiters focus on what they do best: building connections and making smart, strategic choices.
As competition for talent grows, the companies that thrive will be the ones that adopt automation thoughtfully and use it to empower their people. The message is clear: the future of hiring is human and automated — working together to create stronger, smarter organizations.
By giving faster responses, consistent updates, and convenient scheduling. Chatbots answer questions anytime, and candidates can book interviews without delays. This respect for their time builds trust and strengthens employer branding.
No. Automation is great for repetitive, high-volume tasks like screening or scheduling. But recruiters bring empathy, judgment, and cultural insight that machines can’t replicate. The future is about working together, not replacement.
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
A working image carousel with:
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
The platform's objective evaluation revealed issues I never would have spotted:
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.
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:
Each of these seems minor, but together they compound into a poor user experience.
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.
The platform also flagged missing elements that separate hobby projects from professional code:
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.
This is when I discovered the real power of the platform. Here's my process now:
I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.
I can get comprehensive analysis across:
This is where I learn. Each issue includes explanation of why it matters and how to fix it.
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.
After making improvements, I submit my code for evaluation. Now I see:
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.
This experience taught me three critical lessons:
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.
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.
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.
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.
Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:
Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.
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
AI is not replacing developers — it is redefining how code gets created. A growing wave of software professionals now describe what they want in plain English and let AI generate the code. This approach has a name: vibe coding.
Since the term was coined in early 2025, vibe coding has gone from a niche Twitter concept to a mainstream development methodology. A 2025 GitHub survey found that 92% of developers now use AI coding tools in some capacity, and roughly 46% of new code in enterprise environments is AI-generated. Whether you are an experienced engineer, a product manager prototyping an idea, or a recruiter evaluating technical talent, understanding vibe coding is no longer optional.
This guide breaks down what vibe coding means, how it works, the tools driving it, and where it is headed — including its direct impact on developer hiring and technical skills assessment.
Vibe coding is an AI-assisted approach to software development where you describe what you want to build using natural language prompts, and an AI model generates the corresponding code. Instead of writing every function and class manually, you communicate your intent — the "vibe" of what the software should do — and iterate on the AI's output through follow-up prompts and refinements.
The vibe coding meaning centers on a fundamental shift: development becomes intent-driven rather than syntax-driven. You focus on what the software should accomplish, and the AI handles how to write it.
The term "vibe coding" was coined by Andrej Karpathy — former Tesla AI director and OpenAI co-founder — in a February 2025 post on X (formerly Twitter). Karpathy described his workflow as one where he would "fully give in to the vibes, embrace exponentials, and forget that the code even exists." He would describe features in natural language, accept the AI's suggestions, and only course-correct when something broke.
The concept resonated immediately. Within months, "vibe coding" entered mainstream developer vocabulary. By late 2025, Collins Dictionary shortlisted it as a word of the year candidate, signaling just how rapidly the idea moved from AI-insider slang to broad cultural awareness.
Traditional development is syntax-centric. You write precise instructions in a programming language, manage dependencies, and debug line by line. Vibe coding flips this model.
The shift does not eliminate the need for programming knowledge. It changes where that knowledge matters most — from writing code to reviewing, directing, and architecting it.
The process starts with a prompt. You describe the feature, function, or application you want in plain language. For example:
The quality of the output depends heavily on the quality of the prompt. Specific, well-structured prompts with clear constraints produce significantly better results than vague requests.
Once you submit the prompt, the AI model generates the code. This is rarely a one-shot process. The real workflow involves iterative refinement — you review the output, identify gaps or errors, and submit follow-up prompts to adjust.
For instance, after receiving an initial API scaffold, you might prompt: "Add input validation for the email field and return a 422 error for malformed requests." The AI updates the code accordingly. This back-and-forth loop is the core of vibe coding — a conversation between developer intent and AI execution.
AI-generated code must still be tested. This step remains your responsibility. You run unit tests, check edge cases, verify security, and ensure the output aligns with your architectural requirements. Vibe coding accelerates the creation phase, but the validation phase requires the same rigor as traditional development — sometimes more, because AI can produce code that works superficially but contains subtle bugs or inefficiencies.
Several AI tools have become central to the vibe coding workflow:
The most effective vibe coding tools work where developers already spend their time:
A new category of platforms is designed specifically for natural-language-first development:
Vibe coding dramatically compresses the time from idea to working prototype. Tasks that previously required days or weeks of manual development can now be completed in hours. Product managers can build functional demos to validate concepts before committing engineering resources. Founders can present working prototypes to investors instead of slide decks.
People without formal programming training can now build functional applications. A marketer can create a custom data dashboard. A designer can prototype an interactive UI. This democratization of software creation expands who can participate in building technology — though understanding code still matters for anything beyond simple applications.
Vibe coding frees experienced developers from repetitive boilerplate code. Instead of spending time on syntax, bracket matching, and import statements, you focus on higher-level decisions: system architecture, data flow, user experience, and business logic. The mental energy saved on implementation details can be redirected to design and optimization.
For senior engineers, vibe coding is a force multiplier. At National Australia Bank, roughly half of production code is now generated by AWS Q Developer, allowing engineers to focus on architecture and code review. AI handles the scaffolding; the developer handles the judgment. When combined with strong coding interview practices, this shift highlights why architectural thinking is becoming the premium skill in technical hiring.
AI-generated code can introduce security vulnerabilities that are not immediately obvious. Models may produce code with hardcoded credentials, SQL injection susceptibility, or improper input validation — not because the AI is malicious, but because it optimizes for functional correctness over security hardening. Every line of AI-generated code requires the same security review you would apply to code from a junior developer.
Rapid code generation can create architectural debt. AI tools often produce code that works but lacks consistent patterns, proper abstraction, or documentation. Over time, this results in codebases that are difficult to maintain, extend, or debug. The speed advantage of vibe coding can become a liability if teams do not enforce code review standards and architectural guidelines.
AI outputs still require deep, informed review. The developer's role shifts from writer to editor and architect — but that role becomes more critical, not less. Accepting AI-generated code without understanding it creates fragile systems. Organizations that rely on technical assessments to evaluate candidates should now test for code review ability and architectural reasoning, not just the ability to write code from scratch.
Vibe coding is reshaping what it means to be a software developer. Writing code is becoming a smaller portion of the job. Reviewing, directing, and testing AI-generated code — along with system design, architecture decisions, and performance optimization — are where experienced developers add the most value.
This shift affects hiring directly. Companies evaluating technical candidates increasingly need to assess problem-solving and system design skills rather than syntax recall. Platforms designed for AI-assisted technical interviews are adapting their evaluations to reflect this new reality.
A new category of skills is emerging around vibe coding:
Online courses and bootcamps are beginning to incorporate these skills, though formal "vibe coding courses" are still in early stages. The developers who combine traditional programming knowledge with strong AI collaboration skills will be the most valuable hires.
New roles are appearing: AI code reviewer, prompt engineer, AI integration specialist, and agent orchestrator. At the same time, existing roles are evolving. Full-stack developers are expected to leverage AI tools as part of their standard workflow. Companies building candidate sourcing strategies for 2026 are already factoring AI-assisted development skills into their job requirements and screening criteria.
The trajectory is clear: AI is moving from a code-suggestion tool to a full development partner. Agentic AI systems — agents that can plan, execute, test, and iterate autonomously — are being integrated throughout the software development lifecycle. Tools like Replit Agent and Claude Code already operate at this level for simpler tasks. Within the next two years, expect AI agents to handle multi-step feature development with minimal human intervention.
Development toolchains are being redesigned for AI collaboration. APIs are becoming more standardized and self-documenting to improve AI comprehension. CI/CD pipelines are adding AI checkpoints for automated code review. Online coding interview platforms are incorporating AI-generated challenges and real-time code collaboration features that reflect how modern development actually works.
Vibe coding represents a fundamental shift comparable to the move from assembly language to high-level programming languages. It does not eliminate the need for skilled engineers — it raises the floor of what one person can build while raising the ceiling of what matters in professional software development.
The developers who thrive will be those who use AI to amplify their expertise, not replace their understanding. As Karpathy himself noted, the approach works best when you have enough experience to recognize when the AI gets it wrong. For organizations, the imperative is clear: invest in evaluating and developing the architectural, design, and review skills that define great engineering in the vibe coding era.
Vibe coding is reshaping software development from the ground up. By enabling developers and non-developers alike to build software through natural language prompts, it accelerates prototyping, lowers barriers to entry, and shifts the developer's core value toward architecture, review, and system design.
The technology is powerful but not without risks. Security vulnerabilities, technical debt, and the need for human oversight remain real challenges. The most effective teams will be those that combine AI-assisted speed with disciplined engineering practices.
For hiring teams, the implications are immediate. Evaluating candidates on syntax knowledge alone is no longer sufficient. Assessing architectural thinking, code review ability, and AI collaboration skills is now essential. Tools like HackerEarth FaceCode enable real-time technical interviews that test exactly these higher-order skills — ensuring your hiring process keeps pace with how software is actually being built today.
Vibe coding is an AI-assisted software development approach where you use natural language prompts to generate code. Instead of writing every line manually, you describe your intent and an AI model produces the code, which you then review, test, and refine. The term was coined by Andrej Karpathy in February 2025.
Vibe coding is becoming a significant part of software development, especially for prototyping, MVPs, and internal tools. However, complex production systems still require experienced engineers for architecture, security review, and optimization. It is more accurate to view vibe coding as an evolution of the developer's toolkit rather than a complete replacement for traditional development.
Yes. Platforms like Replit Agent, Lovable, and Bolt.new allow people without formal programming training to build functional applications using natural language descriptions. However, building anything beyond simple applications still benefits from understanding programming fundamentals, debugging, and system architecture.
Leading vibe coding tools include GitHub Copilot, Cursor, Claude Code, ChatGPT, Replit Agent, Lovable, and Bolt.new. IDE integrations for VS Code and JetBrains bring AI assistance directly into existing developer workflows. The best tool depends on your use case — Cursor and Claude Code suit experienced developers, while Replit and Lovable target rapid prototyping and beginners.
No. Vibe coding changes what developers spend their time on, shifting the focus from writing code to reviewing, directing, and architecting it. The need for experienced engineers who understand system design, security, and performance optimization increases as AI-generated code becomes more prevalent. Human oversight remains essential for production-quality software.
Yes. Key risks include security vulnerabilities in AI-generated code, accumulation of technical debt from inconsistent code patterns, and the danger of accepting AI output without thorough review. Organizations must maintain rigorous code review standards and security testing regardless of whether code is written by a human or generated by AI.
Cheating in online technical assessments is now an AI problem, not a copy-paste problem. Candidates use ChatGPT to write code, hire stand-ins through Discord servers, run virtual machines to hide secondary screens, and route entire interviews through AI tools that whisper answers in real time. Research from Canvas8 and Multiverse in 2024 found that roughly half of job seekers admit to using generative AI to misrepresent their skills during applications or assessments — a number that has reset what "honest signal" means in technical hiring.
This article covers the tactics candidates actually use, the controls that work against each one, and the trade-offs of every prevention method. Some proctoring techniques degrade candidate experience. Some flag honest candidates. We name those costs where they exist.
The cost of a wrong technical hire has not changed much — Forbes reports that replacing an employee can cost roughly 30% of their annual salary, and several multiples of salary for senior engineering roles. What has changed is the volume of unreliable signal entering the funnel.
Three shifts matter:
Assessments designed to be a signal filter are increasingly a noise filter. That changes what proctoring needs to do.
Most cheating in online technical assessments today falls into four buckets. We've ordered them by how often we see them in customer reports, not by sophistication.
This is the most common cheating method on take-home assignments and unproctored coding tests. Candidates paste the problem into ChatGPT, Claude, or GitHub Copilot, copy the output, and submit. For many common algorithmic problems, LLMs frequently produce solutions that pass standard test cases on the first attempt.
What this looks like in practice: a junior backend candidate submits a clean implementation of a graph traversal problem with idiomatic Python, but cannot explain their choice of data structure in the follow-up interview. The code is correct. The candidate isn't.
What works against it:
Trade-offs to name honestly: restricting copy-paste degrades the experience for candidates who legitimately draft in their own editor. Some senior engineers find this insulting. The fix is to communicate the restriction up front and limit it to junior screens, where the volume justifies the friction.
Proxy candidates are the most expensive form of cheating to detect and the most damaging when missed. The setup ranges from a friend taking the test on the candidate's laptop, to paid services that complete entire interview loops on the candidate's behalf.
What works against it:
Trade-offs: ID verification raises legitimate privacy concerns, and in some jurisdictions (parts of the EU, Illinois under BIPA) it requires explicit consent and data-handling disclosures. Don't deploy without your legal team reviewing the consent flow.
A second laptop on the desk. A phone in the lap. A friend whispering over Discord through earbuds. This is the in-between tier: more effort than ChatGPT, less commitment than a proxy.
What works against it:
Trade-offs: webcam-based proctoring has documented false positive rates that disproportionately affect candidates with darker skin tones, candidates with certain disabilities, and candidates testing in non-ideal home environments. Bias-audit your proctoring vendor's models before deploying at scale. If your vendor can't tell you how their flagging models were tested, switch vendors. For more on designing fair evaluation processes, see our guide on reducing bias in technical hiring.
The most technically sophisticated cheating method. The candidate runs the assessment inside a VM, with their host OS free to search for answers, run a second AI session, or share the screen with a remote helper.
What works against it:
Trade-offs: secure browsers don't run on every OS configuration. Linux users, candidates on locked-down corporate machines, and candidates with accessibility tools sometimes can't complete the assessment. Have a fallback proctored option for these cases — usually a live video interview using a tool like FaceCode.
The right control for cheating in online technical assessments depends on the format. Treating all assessments the same is where most proctoring rollouts go wrong.
Async take-home assignments (the candidate works on their own time, with hours or days to complete) cannot be fully proctored. Accept this. The controls that work here are:
Live proctored coding sessions (the candidate works in a fixed window with monitoring) can apply the full proctoring stack. Use these for:
Live video interviews with an engineer (FaceCode-style) need almost no proctoring beyond ID verification. The interviewer is the proctor. The trade-off is engineering time — according to levels.fyi compensation data, senior engineers at major tech companies command total compensation that translates to well over $100/hour fully loaded, making a 60-minute screen for every applicant unaffordable above a few hundred candidates.
Stopping cheating in online technical assessments looks different at different seniority levels.
For high-volume entry-level and campus hiring, where you screen thousands of candidates for hundreds of offers, automated proctoring with rigorous identity verification is the only economically viable approach. Accept some false positives. Build a human-review queue for flagged sessions. Be transparent with candidates about what is monitored.
For senior engineering hiring, where each candidate is expensive to source and the cost of one bad hire is high, lean on the live interview. Use take-homes as conversation starters, not screening filters. A staff engineer who used AI to draft their take-home and then walks you through the design choices articulately is not the same problem as a junior candidate who pasted ChatGPT output and can't explain it. Modern hiring should be able to tell the difference.
For AI-fluent roles specifically — where the job involves using AI tools — the question isn't whether the candidate used AI on the assessment. It's whether they used it well. The frame shifts from "did they cheat" to "can they do the actual job."
If you are dealing with cheating in online technical assessments at scale, the practical question is how to layer controls without slowing the funnel. HackerEarth's proctoring stack pairs with Skill Assessments and FaceCode to address the four cheating patterns above — a secure browser that restricts VM use and copy-paste, KYC-grade identity verification that confirms the candidate is who they claim to be, and session monitoring that flags irregularities for human review. One enterprise customer used the assessment platform to screen more than 2,000 candidates in a single weekend with consistent rubric-applied evaluation.
The proxy-candidate problem in particular is hard to solve with static tests. OnScreen runs structured AI interviews with built-in identity verification and proctoring, so a candidate has to respond to follow-up questions in real time rather than submit pre-prepared work. As described in HackerEarth's OnScreen launch announcement, Pawan Kuldip, Head of HR at Discover Dollar Inc., noted that the team previously struggled with long interview cycles and unreliable shortlists, and reported that after deploying OnScreen, "roles that previously took much longer are now being closed within three to four weeks," with shortlists that more reliably exclude AI-generated and proxy-completed applications.
How do candidates use ChatGPT to cheat on coding tests? They paste the problem into ChatGPT or Claude, copy the generated solution, and submit it. For standard algorithmic problems (sorting, graph traversal, dynamic programming), modern LLMs produce correct, idiomatic code on the first try. The tell is usually in the follow-up: candidates can't explain choices in code they didn't write. The defense is not detection software — it's interview design that requires the candidate to extend or debug their own solution live.
Does AI-based proctoring invade candidate privacy? AI-based proctoring collects biometric and behavioral data — webcam recording, room scans, ID verification, keystroke patterns — that carries real privacy implications. In the EU, the UK, and several US states, candidates have legal rights to know what is captured and how it is processed. Treat proctoring consent as a real candidate-experience decision, not a checkbox. Tell candidates exactly what is monitored before they start.
How accurate is AI cheating detection? Mixed. VM detection and copy-paste flagging are close to deterministic. Eye-tracking and audio-based flagging produce meaningful false-positive rates, especially for candidates with disabilities, candidates in shared living spaces, and candidates who naturally look away from the screen while thinking. Treat algorithmic flags as input to human review, not as automated rejection.
Can candidates cheat through AI interviews like OnScreen? The counterintuitive risk isn't the candidate gaming the AI in real time — it's candidates rehearsing scripted answers using LLMs in the days before the interview. Adaptive follow-ups and identity verification limit live cheating, but interviewers should still vary question paths and probe for reasoning behind rehearsed-sounding responses. No system catches every cheater; the goal is to make cheating expensive enough that preparing honestly is the cheaper path.
Should we ban AI tools in assessments entirely? Depends on the role. For roles where the job involves using AI daily — which is most software engineering today — banning AI in assessments tests the wrong skill. Evaluate how the candidate uses AI, not whether they avoid it. For roles where AI use during the job is restricted (regulated industries, security-sensitive work), the assessment should mirror that constraint.
Cheating detection reflects a persistent asymmetry: a candidate can adopt a new AI tool in an afternoon, while a hiring team needs weeks to audit, deploy, and tune a counter-control. Any article promising "the solution" is overstating the case. What works is layered defense: design assessments that LLMs struggle with, verify identity with KYC-grade tools, monitor sessions with proctoring you've audited for bias, and always pair high-stakes hires with a live conversation that current AI tools struggle to replicate convincingly in real time.
Schedule a demo of HackerEarth Assessments to see how the secure browser, identity verification, and OnScreen AI interviews work together against the four cheating patterns covered here.
“This hot new field promises to revolutionize industries from business to government, health care to academia,” says the New York Times. People have woken up to the fact that without analyzing the massive amounts of data that’s at their disposal and extracting valuable insights, there really is no way to successfully sustain in the coming years.
Touted as the most promising profession of the century, data science needs business savvy people who have listed data literacy and strategic thinking as their key skills. Anjul Bhambri, VP of Architecture at Adobe, says, “A Data Scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” (She was previously IBM’s VP of Big Data Products.)
How do we get value from this avalanche of data in every sector in the economy? Well, we get persistent and data-mad personnel skilled in math, stats, and programming to weave magic using reams of letters and numbers.
Over the last few years, people have moved away from the umbrella term, data scientist. Companies now advertise for a diverse set of job roles such as data engineers, data architects, business analysts, MIS reporting executives, statisticians, machine learning engineers, and big data engineers.
In this post, you’ll get a quick overview about these exciting positions in the field of analytics. But do remember that companies often tend to define job roles in different ways based on the inner workings rather than market descriptions.
Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization.
Skills Required:
MIS reporting executives typically have degrees in computer science or engineering, information systems, and business management or financial analysis. Some universities also offer degrees in MIS. Look at this image from the University of Arizona which clearly distinguishes MIS from CS and Engineering.
Roles & Responsibilities:
MIS reporting executives meet with top clients and co-workers in public relations, finance, operations, and marketing teams in the company to discuss how far the systems are helping the business achieve its goals, discern areas of concern, and troubleshoot system-related problems including security.
They are proficient in handling data management tools and different types of operating systems, implementing enterprise hardware and software systems, and in coming up with best practices, quality standards, and service level agreements. Like they say, an MIS executive is a “communication bridge between business needs and technology.”
Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity.
Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.
Skills Required:
Apart from a degree in business administration in the field of your choice, say, healthcare or finance, aspiring business analysts need to have knowledge of data visualization tools such as Tableau and requisite IT know-how, including database management and programming.
You could also major in computer science with additional courses that include statistics, organizational behavior, and quality management. Or you could get professional certifications such as the Certified Business Analysis Professional (CBAP®) or PMI Professional in Business Analysis (PBA). Many universities offer degrees in business intelligence, business analytics, and analytics. Check out the courses in the U.S/India.
Roles & Responsibilities:
Business analysts identify business needs, crystallizing the data for easy understanding, manipulation, and analysis via clear and precise requirements documentation, process models, and wireframes. They identify key gaps, challenges, and potential impacts of a solution or strategy.
In a day, a business analyst could be doing anything from defining a business case or eliciting information from top management to validating solutions or conducting quality testing. Business analysts need to be effective communicators and active listeners, resilient and incisive, to translate tech speak or statistical analysis into business intelligence.
They use predictive, prescriptive, and descriptive analysis to transform complex data into easily understood actionable insights for the users. A change manager, a process analyst, and a data analyst could well be doing business analysis tasks in their everyday work.
Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.
Skills Required:
Data analysts are expected to know R, Python, HTML, SQL, C++, and Javascript. They need to be more than a little familiar with data retrieval and storing systems, data visualization and data warehousing using ETL tools, Hadoop-based analytics, and business intelligence concepts. These persistent and passionate data miners usually have a strong background in math, statistics, machine learning, and programming.
Roles & Responsibilities:
Data analysts are involved in data munging and data visualization. If there are requests from stakeholders, data analysts have to query databases. They are in charge of data that is scraped, assuring the quality and managing it. They have to interpret data and effectively communicate the findings.
Optimization is must-know skill for a data analyst. Designing and deploying algorithms, culling information and recognizing risk, extrapolating data using advanced computer modeling, triaging code problems, and pruning data are all in a day’s work for a data analyst. For more information about how a data analyst is different from a data scientist.
Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.
Skills Required:
Typically, statisticians need higher degrees in statistics, mathematics, or any quantitative subject. They need to be mini-experts of the industries they choose to work in. They need to be well-versed in R programming, MATLAB, SAS, Python, Stata, Pig, Hive, SQL, and Perl.
They need to have strong background in statistical theories, machine learning and data mining and munging, cloud tools, distributed tools, and DBMS. Data visualization is a hugely useful skill for a statistician. Aside from industry knowledge and problem-solving and analytical skills, excellent communication is a must-have skill to report results to non-statisticians in a clear and concise manner.
Roles & Responsibilities:
Using statistical analysis software tools, statisticians analyze collected or extracted data, trying to identify patterns, relationships, or trends to answer data-related questions posed by administrators or managers. They interpret the results, along with strategic recommendations or incisive predictions, using data visualization tools or reports.
Maintaining databases and statistical programs, ensuring data quality, and devising new programs, models, or tools if required also come under the purview of statisticians. Translating boring numbers into exciting stories is no easy task!
One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook.
Almost every company has some sort of a data role on its careers page.Job Descriptions for data scientists and data analysts show a significant overlap.
Skills Required:
They are expected to be experts in R, SAS, Python, SQL, MatLab, Hive, Pig, and Spark. They typically hold higher degrees in quantitative subjects such as statistics and mathematics and are proficient in Big Data technologies and analytical tools. Using Burning Glass’s tool Labor Insight, Rutgers students came up with some key insights after running a fine-toothed comb through job postings data in 2015.
Roles & Responsibilities:
Like Jean-Paul Isson, Monster Worldwide, Inc., says, “Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business.” Data scientists come up with queries.
Along with predictive analytics, they also use coding to sift through large amounts of unstructured data to derive insights and help design future strategies. Data scientists clean, manage, and structure big data from disparate sources. These “curious data wizards” are versatile to say the least—they enable data-driven decision making often by creating models or prototypes from trends or patterns they discern and by underscoring implications.
“Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.
Skills Required:
Data engineers are computer engineers who must know Pig, Hadoop, MapReduce, Hive, MySQL, Cassandra, MongoDB, NoSQL, SQL, Data streaming, and programming. Data engineers have to be proficient in R, Python, Ruby, C++, Perl, Java, SAS, SPSS, and Matlab.
Other must-have skills include knowledge of ETL tools, data APIs, data modeling, and data warehousing solutions. They are typically not expected to know analytics or machine learning.
Roles & Responsibilities:
Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Unlike data scientists who seek an exploratory and iterative path to arrive at a solution, data engineers look for the linear path. Data engineers will improve existing systems by integrating newer data management technologies.
They will develop custom analytics applications and software components. Data engineers collect and store data, do real-time or batch processing, and serve it for analysis to data scientists via an API. They log and handle errors, identify when to scale up, ensure seamless integration, and “build human-fault-tolerant pipelines.” The career path would be Data Engineer?Senior Data Engineer?BI Architect?Data Architect.
Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.
Skills Required:
Engineers should focus on Python, Java, Scala, C++, and Javascript. To become a machine learning engineer, you need to know to build highly-scalable distributed systems, be sure of the machine learning concepts, play around with big datasets, and work in teams that focus on personalization.
ML engineers are data- and metric-driven and have a strong foundation in mathematics and statistics. They are expected to have experience in Elasticsearch, SQL, Amazon Web Service, and REST APIs. As always, great communication skills are vital to interpret complex ML concepts to non-experts.
Roles & Responsibilities:
Machine learning engineers have to design and implement machine learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges. ML engineers build data pipelines, benchmark infrastructure, and do A/B testing.
They work collaboratively with product and development teams to improve data quality via tooling, optimization, and testing. ML engineers have to monitor the performance and ensure the reliability of machine learning systems in the organization.
What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.
Skills Required:
Big data engineers, who have computer engineering or computer science degrees, need to know basics of algorithms and data structures, distributed computing, Hadoop cluster management, HDFS, MapReduce, stream-processing solutions such as Storm or Spark, big data querying tools such as Pig, Impala and Hive, data integration, NoSQL databases such as MongoDB, Cassandra, and HBase, frameworks such as Flume and ETL tools, messaging systems such as Kafka and RabbitMQ, and big data toolkits such as H2O, SparkML, and Mahout.
They must have experience with Hortonworks, Cloudera, and MapR. Knowledge of different programming and scripting languages is a non-negotiable skill. Usually, people with 1 to 3 years of experience handling databases and software development is preferred for an entry-level position.
Roles & Responsibilities:
Rijmenam says “Big data engineers develop, maintain, test, and evaluate big data solutions within organizations. Most of the time they are also involved in the design of big data solutions, because of the experience they have with Hadoop[-]based technologies such as MapReduce, Hive, MongoDB or Cassandra.”
To support big data analysts and meet business requirements via customization and optimization of features, big data engineers configure, use, and program big data solutions. Using various open source tools, they “architect highly scalable distributed systems.” They have to integrate data processing infrastructure and data management.
It is a highly cross-functional role. With more years of experience, the responsibilities in development and operations; policies, standards and procedures; communication; business continuity and disaster recovery; coaching and mentoring; and research and evaluation increase.
Companies are running helter-skelter looking for experts to draw meaningful conclusions and make logical predictions from mammoth amounts of data. To meet these requirements, a slew of new job roles have cropped up, each with slightly different roles & responsibilities and skill requirements.
Blurring boundaries aside, these job roles are equally exciting and as much in demand. Whether you are a data hygienist, data explorer, data modeling expert, data scientist, or business solution architect, ramping up your skill portfolio is always the best way forward.
Look at these trends from Indeed.com
If you know exactly what you want to do with your coveted skillset comprising math, statistics, and computer science, then all you need to do is hone the specific combination that will make you a name to reckon with in the field of data science or data engineering.
To read more informative posts about data science and machine learning, go here.
“Just keep it simple, silly!”
It is often both exciting and intimidating while starting to learn new stuff, but it is also the only way to keep us updated, with one foot in the future.
Although there is so much of frenzy around Mixed Reality these days, there are really very few developers currently working on AR/VR.
While there already is a lot of shortage of tech talent worldwide, the scales of demand and supply are even more skewed in this particular category.
Here is all that you need to know to start building your first application for Virtual Reality:
Start by Exploring
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Before you sit down and start developing, you’ll need to know a little bit about the ecosystem itself and the scope of the technology in the current landscape. You do that by spending enough time exploring new applications and following relevant industry insiders, developers, and media channels who are already neck deep in the stuff and listen to what they have to say.
For more information on getting started with virtual reality, go here.
Get your tools right
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Don’t worry about having the latest and most expensive VR Gear in the beginning. Just get yourself a $10 Google Cardboard; that’ll be enough to get you started. Also, do not download the latest version of Unity or Google VR as they usually come with several bugs and need a lot of fixing which at the beginning might leave you stumped. Go for the most stable build out there.
The most stable build at the time of publishing of this article is:
Unity3d 5.4.2: Unity – Get Unity – Download Archive
Google VR 1.03: Google VR SDK for Unity
(To download GVR 1.03: Go to the above link. Then, click on “33 commits” under where it says Google VR SDK for Unity and above where it says Branch: master.
Where it says GVR Unity SDK v1.0.3, click on the “< >” button on the far right. That takes you to a version of the repository from a previous date, v1.0.3 in this case.
Finally, click on the green “Clone or download button” and select Download Zip.
*It’ll be a big file of size around 800 MB, but you’ll just need the Unity package which is about 50 MB; you’ll have to download the complete file though, then delete the rest of it later.)
Form a routine
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You might have classes or a job, but if you don’t take some time out every day, then with each day that you don’t get hands on the tools, it will push you back a lot and the next time you open the tools, you might have a hard time figuring out where you left. You might have to start all over again!
What I did and what I recommend is committing at least half an hour each day to familiarizing yourself with the gaming engine you’re working on whether you are able to make significant progress each day or not.
Also, so that you don’t lose focus and interest along the way, I suggest you form a small group of 3–5 friends who are also interested in the space on Whatsapp, Facebook, or wherever you like and make it a point to share at least one article that you come across each day in that group. It will force you to develop a habit of reading something related every day. Do it even if nobody else seems to be participating.
Practice, Practice, Practice
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This is a no brainer; most development on VR depends on how well you know your gaming engine (Unity in this case) and a little bit of programming. And no matter what you think you’ll typically have to put in at least 40–50 hours of productive time on getting a hang of these tools before you can really start building your own stuff. A lot of tutorials are available online which will guide you while making your first application without much of a background.
I suggest that you read as many tutorials as you can to understand these tools. You will be deceiving yourself if you believe that you’ve got the hang of the tools the first time you follow the tutorial and what you create will actually work. You’ll soon discover that it will be hard for you to recall any of it a day or two later, Don’t pull out your hair if your application doesn’t come up as they taught in the tutorial, as what’ll happen most of the time is you’ll have different machines, different SDKs, and different versions of packages that you’ll be using. So, it will be only natural if something doesn’t work quite as it does in the tutorial.
Combinatory Approach
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Do what you know. After spending some time on the tutorials, you’ll have learned a couple of tricks and will be comfortable implementing some of the functionalities really well. Just focus on that and nothing else. Don’t go into your first project with a specific idea. ?Learn what you can do and design an application around that. Don’t let a specific idea bog you down. Remember this, your first project is supposed to be something of a disaster, and there is no shame in accepting that. You’ll get only better from there.
Start small
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Learn that complex applications are made with huge teams over a long duration. Starting off the first time, you might not necessarily have the correct frame of reference of how long it is going to take you to build stuff, so don’t plan any project which you think is going to take more than a couple of weeks. For example, if you’re building a game like Mario, then do so by stripping all the unnecessary items like keeping it to just one level only without any enemies to dodge or points to collect and other additional conditions; just focus on the minimum number of things that will make it work, say, setting the environment and making your player jump and move. You’ll be surprised that the first time around even this is going to take you a huge amount of time. You can always add things later on. Your first application is going to take you an amount of time that is cumulative of all the practice time that you put in and even more. But just stick to it and your routine; it is a part of the process and don’t be afraid to write a little bit of code. If you design it right, you’ll need to do very little of coding anyway to get anything done.
Don’t worry about making it look pretty; worry about it working at all
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When you are just getting started, you’ll want to go in with the idea of everything being perfect. Just make a note that it is anyway going to be time consuming and also difficult the first time, so do not waste your energy trying to make it look good, just keep striving toward making it work at all. The first ever VR application that I built on my own was very rudimentary, yet it took me about a month to build it, and a lot of time was spent figuring out small details. When you get started, you’ll realize that most of the time it will be the small stuff that keeps you from moving ahead. Sometimes, things won’t work because a library that you’re working on doesn’t exist anymore or because an update in the SDK makes a lot of what you’ve done irrelevant. This is part of the process as well, allowing you to learn the details of the process of building.
Here is the link of the application that I built:
https://play.google.com/store/apps/details?id=com.Pratham.ShootemVR
The project it open source, use it to learn and build if you want to. Here is the link to the assets: https://github.com/pratham2504/Shoot-em-VR
Set milestones
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Even though it sounds obvious, you’ll be surprised how easy is it to let days go by without getting much stuff done. Setting small daily milestones will help. Don’t wait if you are not making progress; just ask around or in sometime you’ll convince yourself that it is not working and just quit. Use forums and repositories such as Stackoverflow or Unity Community resources or just ask people at random who’s working on similar things. Most of the time, you’ll find help.
Just remember, anything worth doing is a struggle but if you stick to it, you’ll eventually get there.
And have fun while learning and building cool stuff. You’ll be glad when you show what you built to your friends for the first time even if it may not look all that pretty.
Excited about VR?
Register at the UnitedByHCl hackathon
Happy mixing reality!
“What is imagination?…It is a God-like, a noble faculty. It renders earth tolerable; it teaches us to live, in the tone of the eternal.” – Ada Lovelace to Charles Babbage
When Charles Babbage, in 1837, proposed a ”Fully programmable machine” which would be later called an Analytical engine, not even the government who seed-funded his Difference Engine believed him.
Undoubtedly the most influential machine in existence in today’s modern computer.
But back in the 19th century, when the world was drooling over the industrial revolution and railway tracks and steam engines, a machine which could think and calculate looked like a distant dream.
While most see the evolution of these advanced machines such as computers and smartphones as examples of electronic innovation, what people have taken for granted had been an evolution and the hard work of transforming a mechanical device into a self-thinking smart device which would become an integral part of our lives.
In the 19th century, the concept of specialization had not breached the revered halls of universities and laboratories.
Most of the geniuses were polymaths, so was the Englishman Charles Babbage. Charles Babbage was a renowned mathematician, philosopher, and mechanical engineer of his times.
During those days, mathematical tables (such as your logbook) were manually made and were used in navigation, science, and engineering.
Since most of these tables were manually updated and calculated, the values in these tables varied frequently, giving inconsistent results during studies.
While at Cambridge, Charles Babbage noticed this flaw and thought of converting this mathematical-table based calculation into a mechanical product to avoid any discrepancies.
In 1822, Charles Babbage decided to make a machine to calculate the polynomial function—a machine which would calculate the value automatically.
In 1823, the British government gave Charles Babbage £1700 (probably the first ever seed funding).
He named it the Difference Engine, possibly after the finite difference method is used to calculate.
Charles Babbage invited Joseph Clement to design his ambitious massive difference engine that had about 25,000 parts, weighed around 15 tons, and was 8 feet tall.
Despite the ample funding by the government, the engine never got completed. And in the late 1840s, he planned on making an improved engine.
But that was not completed either due to lack of funds.
In 1989–1991, scientists and engineers studying Charles Babbage’s research paper built the first difference engine, which is now placed in The Museum of the History of Science, Oxford.
Let’s take an example with a polynomial function R = x2 + 1
| X | R | Difference 1 | Difference 2 | |
| Step 1 | 0 | 1 | 1 (D11) | 2 (D21) |
| Step 2 | 1 | 2 | 3 (D12) | 2 (D22) |
| Step 3 | 2 | 5 | 5 (D13) | 2 (D23) |
| Step 4 | 3 | 10 | 7 (D14) | 2 (D24) |
| Step 5 | 4 | 17 | 9 (D15) | 2 (D2) |
To solve this manually, you need to solve the equation “n+1” times, where n is the polynomial. So, for the given equation, we need threesteps.
When X = 0, result of R = 1; X= 1, R =2; X=2, R= 5, and so on.
So for the Difference 1 column in the table above,
D11 = 2 (R2) – 1(R1) = 1
D12 = 5 (R3) – 2(R2) = 3
D13 = 10 (R4) – 5(R3) = 5
By subtracting two consecutive values from the Difference 1 column,
D21 = 3 (D12) – 1(D11) = 2
D22 = 5 (D13) – 3 (D12) = 2
Similarly, for a third-order equation, we can prepare a new column called Difference 3, and calculate it by subtracting two consecutive numbers from the last column.
*The values in the last column or the highest power value always remain constant in the last difference column.*
Since the engine could only add and subtract, some of the values from each column are given to the difference engine to feed the engine with information necessary for further calculations.
Let’s take another example where you have to calculate the result for x = 3 from the above equation (R = x2 + 1), and the engine was already given the values of Step 1 and Step 2 columns (Refer to above table). The engine would follow the following steps:
Step 1: To calculate the value for D12, Step 1 difference 2 is added to Step 1 Difference 1, which is 2(D21) +1( D11)=3.
Step 2: This D12 when added with R2, which gives the result for Step 3 = 3 (D12) + 2( R2) = 5
Similarly, to calculate the result for x = 4
Step 1 – For X = 4, Step 2 – Difference 2 added to Difference 1 = 2 (D22) +1 (D12) = 5
Step 2 – Add value from Step 1 to Step 3 result R3, which is 5+5, giving the final value as 10
A difference engine (shown above) consisted of N+1 columns, where column N could only store constants and Column 1 showed the value of the current iteration.
And the machine was only capable of adding values from column n+1 to N.
The engine is programmed by setting initial values to the columns. Column 1 is set to the value of the polynomial at the start of computation.
Column 2 is set to a value derived from the first and higher derivatives of the polynomial at the same value of X.
Each column from 3 to N is set to a value derived from the first-order derivative. To simplify what the difference engine did, here is a simple code for Polynomial Function calculation using C++ –
#include <iostream>
#include <math.h>
using namespace std;
int d; // degree of the polynomial
int i; //
int c; //
int value;
int j;
int p;
int sum;
int main()
{
cout << "Enter the degree of the polynomial: " << endl;
cin >> d; // degree of the polynomial
cout << "The degree of the polynomial you entered was " << d << endl;
int *c = new int[i];
for(i = 0; i <= d; i++)
{
cout << "Enter coefficients: " << endl;
cin >> c[i];
int c[d+1];
}
cout << "There are " << d + 1 << " coefficients";
cout << " The coefficients are: ";
for (i = 0; i < d + 1; i++)
cout << "\n " << c[i];
cout << endl;
cout << " Enter the value for evaluating the polynomials" << endl;
cin >> value;
sum = 0;
cout << " The value is " << value << endl;
cout << "First polynomial is: " << endl;
cout << c[0] << "x^3 + " << c[1] << "x^2 + " << c[2] << "x + " << c[3] << endl;
{
for (i = 0; i <= d; i++)
p = 1;
{
for (j = 0; j <= (d - 1); j++)
p = p * value;
sum = sum + p;
sum = pow(c[0]*value,d)+pow(c[1]*value,d-1)+pow(c[2]*value,d-2)+pow(c[3]*value,d-3);
cout << "The sum is " << sum << endl;
}
}
}
The difference engine was never finished, and during its construction, Charles Babbage had a brilliant idea of using Punch Cards for calculation.
Till then, punch cards that had been used only for the mundane job of weaving would form the basis of future computer programming.
Before Joseph Jacquard came up with the idea of punch cards, the weaving was done using draw looms. A drawloom generally used a “figure harness” to control the weaving pattern.
The drawloom required two operators to control the machine.
Although till 1801, punch cards were only used for individual weaving, Jacquard decided to use perforated papers with the mechanism, because he found that though being intricate, weaving was mechanical and repetitive.
In the most basic form, a weaving design is made by passing onethread over another.
In a patterned weave, the threads crossing each other are not synchronized by equal blocks but are changed according to the required pattern.
A weaver controls the threads by pulling and releasing them.
When Joseph Jacquard came up with the idea of a loom, the fabric design in it was first copied on square papers.
This design on the square was translated into punch cards. These cards are stitched together in a continuous belt and fed into the loom.
The holes in the card controlled which threads are raised into the weaving pattern.
This automation allowed Jacquard to make designs and produce them again at lesser costs. Keeping this bunch of cards helped to reproduce the same design repeatedly with perfection on the same or another machine.
“Visualizing” the concept of using these punch cards to calculate, Charles Babbage described using them for the analytical engine.
In 1883, Charles Babbage was introduced to ayoung brilliant mathematician, Ada, who later became Countess of Lovelace, byher tutor.
He was impressed with Ada’sanalytical skills and invited her to look the difference engine, which fascinated her.
This formed the basis of a lasting friendship that continued until her death.
Born to British poet Lord Byron and Annabella Milbanke, Augusta Ada Byron married William King-Noel, who was the first Earl of Lovelace.
Ada was a natural poet who found mathematics poetic.
Growing up, Ada’s education and her families’ influential presence got her in touch with a few prestigious innovators and literary figures of her time.
While studying mathematics, her tutor Mary Somerville introduced her to Charles Babbage, who, after his work on the unsuccessful Difference Engine, was working on an ambitious project of a machine which could solve any complex mathematical function (the Analytical Engine).
What you see below is a caricature image of the Analytical Engine as proposed by Charles Babbage.
The important parts of this engine still constitute our modern computers.
Ada, impressed by the theory and concept of the Analytical Engine, decided to work with Charles Babbage onthe construction of the engine.
During her study of the Analytical Engine, she wrote a series of notes which explained the difference between a Difference Engine and an Analytical Engine.
She took up Bernoulli number theory and built a detailed algorithm on the process of calculating Bernoulli numbers using an Analytical engine which was demonstrated in Note G of her article shown below.
This made her the first programmer in the world. (This is disputed.)
Though her notes were never accepted, and as there was no funding or investment to back Charles Babbage’s fantastic idea, the analytical engine was never completed.
Here is a simple C++ program to the algorithm developed by Ada Lovelace in her lengthy notes:
// bernoulli_distribution
#include <iostream>
#include <random>
int main()
{
const int nrolls=10000;
std::default_random_engine generator;
std::bernoulli_distribution distribution(0.5);
int count=0; // count number of trues
for (int i=0; i<nrolls; ++i) if (distribution(generator)) ++count;
std::cout << "bernoulli_distribution (0.5) x 10000:" << std::endl;
std::cout << "true: " << count << std::endl;
std::cout << "false: " << nrolls-count << std::endl;
return 0;
Charles Babbage declined both the title of Knighthood and baronetcy and instead asked for a life peerage, but that wish wasn’t granted in his lifetime.
He died in 1871 ate the age of 79. Ada Lovelace died at the young age of 36 in 1852.
Her contribution to computer science for having come up with the “first” algorithm still remains one of the greatest controversies in technology history.
You can read one such article here.
Irrespective of these facts, their contribution to the field of computer and programming cannot be ignored.
A super calculator which would be able to solve any mathematical problem and a device which would have the ability to think of ways to approach a problem is what Charles Babbage and Ada Lovelace thought of; this was the founding stone of the first programmable computer.
In the next article, we will discuss the use of Punch Cards and how with all technological developments in Europe, the USA got the first computer!
Artificial Intelligence (AI) has permeated virtually every industry, transforming operations and interactions. The tech recruitment sector is no exception, and AI’s influence shapes the hiring processes in revolutionary ways. From leveraging AI-powered chatbots for preliminary candidate screenings to deploying machine learning algorithms for efficient resume parsing, AI leaves an indelible mark on tech hiring practices.
Yet, amidst these promising advancements, we must acknowledge the other side of the coin: AI’s potential malpractices, including the likelihood of cheating on assessments, issues around data privacy, and the risk of bias against minority groups.
The introduction of AI in recruitment, while presenting significant opportunities, also brings with it certain drawbacks and vulnerabilities. Sophisticated technologies could enable candidates to cheat on assessments, misrepresent abilities and potential hiring mistakes. This could lead to hiring candidates with falsifying skills or qualifications, which can cause a series of negative effects like:
Your company could be left exposed to significant risks if your AI recruiting software is not robust enough to protect sensitive employee information. The implications for an organization with insufficient data security could be severe such as:
Perhaps the most critical issue of all is the potential for unconscious bias. The potential for bias in AI recruiting software stems from the fact that these systems learn from the data they are trained on. If the training data contains biases – for example, if it reflects a history of preferentially hiring individuals of a certain age, gender, or ethnicity – the AI system can learn and replicate these biases.
Even with unbiased data, if the AI’s algorithms are not designed to account for bias, they can inadvertently create it. For instance, a hiring algorithm that prioritizes candidates with more years of experience may inadvertently discriminate against younger candidates or those who have taken career breaks, such as for child-rearing or health reasons.
This replication and possible amplification of human prejudices can result in discriminatory hiring practices. If your organization’s AI-enabled hiring system is found to be biased, you could face legal action, fines, and penalties. Diversity is proven to enhance creativity, problem-solving, and decision-making. In contrast, bias in hiring can lead to a homogenous workforce, so its absence would likely result in a less innovative and less competitive organization.
Also read: What We Learnt From Target’s Diversity And Inclusion Strategy
How do you evaluate the appropriateness of using AI in hiring for your organization? Here are some strategies for navigating the AI revolution in HR. These steps include building support for AI adoption, identifying HR functions that can be integrated with AI, avoiding potential pitfalls of AI use in HR, collaborating with IT leaders, and so on.
Despite certain challenges, AI can significantly enhance tech recruitment processes when used effectively. AI-based recruitment tools can automate many manual recruiting tasks, such as resume screening and interview scheduling, freeing up time for recruiters to focus on more complex tasks. Furthermore, AI can improve the candidate’s experience by providing quick responses and personalized communications. The outcome is a more efficient, candidate-friendly process, which could lead to higher-quality hires.
Let’s look at several transformational possibilities chatbots can bring to human capital management for candidates and hiring teams. This includes automation and simplifying various tasks across domains such as recruiting, onboarding, core HR, absence management, benefits, performance management, and employee self-service resulting in the following:
Also read: 5 Steps To Create A Remote-First Candidate Experience In Recruitment
Additionally, candidates can leverage these AI-powered chatbots in a dialog flow manner to carry out various tasks. These tasks include the following:
These can also be utilized by your tech hiring teams for various purposes, such as:
Other AI recruiting technologies can also enhance the hiring process for candidates and hiring teams in the following ways:
These enhancements not only streamline the hiring process but also improve the quality of hires, reduce hiring biases, and improve the experience for everyone involved. The use of AI in hiring can indeed take it to the next level.
AI can dramatically reshape the recruitment landscape with the following key advancements:
Blockchain technology, renowned for its secure, transparent, and immutable nature, can revolutionize background checks. This process which can take anywhere from between a day to several weeks today for a single recruiter to do can be completed within a few clicks resulting in:
VR can provide immersive experiences that enhance various aspects of the tech recruitment process:
Also read: 6 Strategies To Enhance Candidate Engagement In Tech Hiring (+ 3 Unique Examples)
To summarize, AI in recruitment is a double-edged sword, carrying both promise and potential problems. The key lies in how recruiters use this technology, leveraging its benefits while vigilantly managing its risks. AI isn’t likely to replace recruiters or HR teams in the near future. Instead, you should leverage this tool to positively impact the entire hiring lifecycle.
With the right balance and careful management, AI can streamline hiring processes. It can create better candidate experiences, and ultimately lead to better recruitment decisions. Recruiters should continually experiment with and explore generative AI. To devise creative solutions, resulting in more successful hiring and the perfect fit for every open role.
“Every hire is an investment for a company. A good hire will give you a higher ROI; if it is a bad hire, it will cost you a lot of time and money.”
Especially in tech hiring!
An effective tech recruitment process helps you attract the best talents, reduce hiring costs, and enhance company culture and reputation.
Businesses increasingly depend on technical knowledge to compete in today’s fast-paced, technologically driven world. Online platforms that provide technical recruiting solutions have popped up to assist companies in finding and employing top talent in response to this demand.
The two most well-known platforms in this field are HackerEarth and Mettl. To help businesses make wise choices for their technical employment requirements, we will compare these two platforms’ features, benefits, and limitations in this article.
This comparison of Mettl alternative, HackerEarth and Mettl itself, will offer helpful information to help you make the best decision, whether you’re a small company trying to expand your tech staff or a massive organization needing a simplified recruiting process.
HackerEarth is based in San Francisco, USA, and offers enterprise software to aid companies with technical recruitment. Its services include remote video interviewing and technical skill assessments that are commonly used by organizations.
HackerEarth also provides a platform for developers to participate in coding challenges and hackathons. In addition, it provides tools for technical hiring such as coding tests, online interviews, and applicant management features. The hiring solutions provided by HackerEarth aid companies assess potential employees’ technical aptitude and select the best applicants for their specialized positions.
Mettl, on the other hand, offers a range of assessment solutions for various industries, including IT, banking, healthcare, and retail. It provides online tests for coding, linguistic ability, and cognitive skills. The tests offered by Mettl assist employers find the best applicants for open positions and make data-driven recruiting choices. Additionally, Mettl provides solutions for personnel management and staff training and development.
Because HackerEarth makes technical recruiting easy and fast, you must consider HackerEarth for technical competence evaluations and remote video interviews. It goes above and beyond to provide you with a full range of functions and guarantee the effectiveness of the questions in the database. Moreover, it is user-friendly and offers fantastic testing opportunities.
The coding assessments by HackerEarth guarantee the lowest time consumption and maximum efficiency. It provides a question bank of more than 17,000 coding-related questions and automated test development so that you can choose test questions as per the job role.
As a tech recruiter, you may need a clear understanding of a candidate’s skills. With HackerEarth’s code replay capability and insight-rich reporting on a developer’s performance, you can hire the right resource for your company.
Additionally, HackerEarth provides a more in-depth examination of your recruiting process so you can continuously enhance your coding exams and develop a hiring procedure that leads the industry.
HackerEarth and Mercer Mettl are the two well-known online tech assessment platforms that provide tools for managing and performing online examinations. We will examine the major areas where HackerEarth outperforms Mettl, thereby proving to be a great alternative to Mettl, in this comparison.
Also read: What Makes HackerEarth The Tech Behind Great Tech Teams
HackerEarth believes in upgrading itself and providing the most effortless navigation and solutions to recruiters and candidates.
HackerEarth provides various tools and capabilities to create and administer online tests, such as programming tests, multiple-choice questions, coding challenges, and more. The software also has remote proctoring, automatic evaluation, and plagiarism detection tools (like detecting the use of ChatGPT in coding assessments). On the other side, Mettl offers comparable functionality but has restricted capabilities for coding challenges and evaluations.
HackerEarth: It has a user-friendly interface that is simple to use and navigate. It makes it easy for recruiters to handle evaluations without zero technical know-how. The HackerEarth coding platform is also quite flexible and offers a variety of pre-built exams, including coding tests, aptitude tests, and domain-specific examinations. It has a rich library of 17,000+ questions across 900+ skills, which is fully accessible by the hiring team. Additionally, it allows you to create custom questions yourself or use the available question libraries.
Also read: How To Create An Automated Assessment With HackerEarth
Mettl: It can be challenging for a hiring manager to use Mettl efficiently since Mettl provides limited assessment and question libraries. Also, their team creates the test for them rather than giving access to hiring managers. This results in a higher turnaround time and reduces test customization possibilities since the request has to go back to the team, they have to make the changes, and so forth.
HackerEarth: You may assess applicant performance and pinpoint areas for improvement with the help of HackerEarth’s full reporting and analytics tools. Its personalized dashboards, visualizations, and data exports simplify evaluating assessment results and real-time insights.
Most importantly, HackerEarth includes code quality scores in candidate performance reports, which lets you get a deeper insight into a candidate’s capabilities and make the correct hiring decision. Additionally, HackerEarth provides a health score index for each question in the library to help you add more accuracy to your assessments. The health score is based on parameters like degree of difficulty, choice of the programming language used, number of attempts over the past year, and so on.
Mettl: Mettl online assessment tool provides reporting and analytics. However, there may be only a few customization choices available. Also, Mettle does not provide code quality assurance which means hiring managers have to check the whole code manually. There is no option to leverage question-based analytics and Mettl does not include a health score index for its question library.
Adopting this platform may be challenging if you want highly customized reporting and analytics solutions.
Also read: HackerEarth Assessments + The Smart Browser: Formula For Bulletproof Tech Hiring
HackerEarth: The security and privacy of user data are top priorities at HackerEarth. The platform protects data in transit and at rest using industry-standard encryption. Additionally, all user data is kept in secure, constantly monitored data centers with stringent access controls.
Along with these security measures, HackerEarth also provides IP limitations, role-based access controls, and multi-factor authentication. These features ensure that all activity is recorded and audited and that only authorized users can access sensitive data.
HackerEarth complies with several data privacy laws, such as GDPR and CCPA. The protection of candidate data is ensured by this compliance, which also enables businesses to fulfill their legal and regulatory responsibilities.
Mettl: The security and data privacy features of Mettl might not be as strong as those of HackerEarth. The platform does not provide the same selection of security measures, such as IP limitations or multi-factor authentication. Although the business asserts that it complies with GDPR and other laws, it cannot offer the same amount of accountability and transparency as other platforms.
Even though both HackerEarth and Mettl include security and data privacy measures, the Mettle alternative, HackerEarth’s platform is made to be more thorough, open, and legal. By doing this, businesses can better guarantee candidate data’s security and ability to fulfill legal and regulatory requirements.
HackerEarth: To meet the demands of businesses of all sizes, HackerEarth offers a variety of customizable pricing options. The platform provides yearly and multi-year contracts in addition to a pay-as-you-go basis. You can select the price plan that best suits their demands regarding employment and budget.
HackerEarth offers chat customer support around the clock. The platform also provides a thorough knowledge base and documentation to assist users in getting started and troubleshooting problems.
Mettl: The lack of price information on Mettl’s website might make it challenging for businesses to decide whether the platform fits their budget. The organization also does not have a pay-as-you-go option, which might be problematic.
Mettl offers phone and emails customer assistance. However, the business website lacks information on support availability or response times. This lack of transparency may be an issue if you need prompt and efficient help.
HackerEarth: The interface on HackerEarth is designed to be simple for both recruiters and job seekers. As a result of the platform’s numerous adjustable choices for test creation and administration, you may design exams specifically suited to a job role. Additionally, the platform provides a selection of question types and test templates, making it simple to build and take exams effectively.
In terms of the candidate experience, HackerEarth provides a user-friendly interface that makes navigating the testing procedure straightforward and intuitive for applicants. As a result of the platform’s real-time feedback and scoring, applicants may feel more motivated and engaged during the testing process. The platform also provides several customization choices, like branding and message, which may assist recruiters in giving prospects a more exciting and tailored experience.
Mettl: The platform is intended to have a steeper learning curve than others and be more technical. It makes it challenging to rapidly and effectively construct exams and can be difficult for applicants unfamiliar with the platform due to its complex interface.
Additionally, Mettl does not provide real-time feedback or scoring, which might deter applicants from participating and being motivated by the testing process.
Also read: 6 Strategies To Enhance Candidate Engagement In Tech Hiring (+ 3 Unique Examples)
According to G2, HackerEarth and Mettl have 4.4 reviews out of 5. Users have also applauded HackerEarth’s customer service. Many agree that the staff members are friendly and quick to respond to any problems or queries. Overall, customer evaluations and feedback for HackerEarth point to the platform as simple to use. Both recruiters and applicants find it efficient.
Mettl has received mixed reviews from users, with some praising the platform for its features and functionality and others expressing frustration with its complex and technical interface.
Recruiting and selecting the ideal candidate demands a significant investment of time, attention, and effort.
This is where tech recruiting platforms like HackerEarth and Mettl have got you covered. They help streamline the whole process.Both HackerEarth and Mettl provide a wide variety of advanced features and capabilities for tech hiring.
We think HackerEarth is the superior choice. Especially, when contrasting the two platforms in terms of their salient characteristics and functioning. But, we may be biased!
So don’t take our word for it. Sign up for a free trial and check out HackerEarth’s offerings for yourself!
Our AI-enabled Smart Browser takes frequent snapshots via the webcam, throughout the assessment.
Consequently, it is impossible to copy-paste code or impersonate a candidate.The browser prevents the following
candidate actions and facilitates thorough monitoring of the assessment:In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.
In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.
Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.
In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.
AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams
All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.
Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.
Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.
Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.
Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.
Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.
Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.
Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.
In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.
Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.
Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.
The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.
Despite its advantages, headhunting also presents certain challenges:
Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.
Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.
Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.
Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.
Headhunting offers several advantages over traditional recruitment methods:
Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.
Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.
Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.
Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.
Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.
The job industry is not the same as it was 30 years ago. Progresses in AI and automation have created a new work culture that demands highly skilled professionals who drive innovation and work efficiently. This has led to an increase in the number of companies reaching out to external sources of recruitment for hiring talent. Over the years, we have seen several job aggregators optimise their algorithms to suit the rising demand for talent in the market and new players entering the talent acquisition industry. This article will tell you all about how external sources of recruitment help companies scout some of the best candidates in the industry, the importance of external recruitment in organizations across the globe and how it can be leveraged to find talent effectively.
External sources refer to recruitment agencies, online job portals, job fairs, professional associations and any other organizations that facilitate seamless recruitment. When companies employ external recruitment sources, they access a wider pool of talent which helps them find the right candidates much faster than hiring people in-house. They save both time and effort in the recruitment process.
Online resume aggregators like LinkedIn, Naukri, Indeed, Shine, etc. contain a large database of prospective candidates. With the advent of AI, online external sources of recruitment have optimised their algorithms to show the right jobs to the right candidates. Once companies figure out how to utilise job portals for recruitment, they can expedite their hiring process efficiently.
Ours is a generation that thrives on social media. To boost my IG presence, I have explored various strategies, from getting paid Instagram users to optimizing post timing and engaging with my audience consistently. Platforms like FB an IG have been optimized to serve job seekers and recruiters alike. The algorithms of social media platforms like Facebook and Instagram have been optimised to serve job seekers and recruiters alike. Leveraging them to post well-placed ads for job listings is another way to implement external sources of recruitment strategies.
Referrals are another great external source of recruitment for hiring teams. Encouraging employees to refer their friends and acquaintances for vacancies enables companies to access highly skilled candidates faster.
Hiring freshers from campus allows companies to train and harness new talent. Campus recruitment drives are a great external recruitment resource where hiring managers can expedite the hiring process by conducting screening processes in short periods.
Companies who are looking to fill specific positions with highly skilled and experienced candidates approach external recruitment agencies or executive headhunters to do so. These agencies are well-equipped to look for suitable candidates and they also undertake the task of identifying, screening and recruiting such people.
This is a win-win situation for job seekers and hiring teams. Job fairs allow potential candidates to understand how specific companies work while allowing hiring managers to scout for potential candidates and proceed with the hiring process if possible.
The role of recruitment agencies in talent acquisition is of paramount importance. They possess the necessary resources to help companies find the right candidates and facilitate a seamless hiring process through their internal system. Here is how external sources of recruitment benefit companies.
External recruitment resources are a great way for companies to hire candidates with diverse professional backgrounds. They possess industry-relevant skills which can be put to good use in this highly competitive market.
Candidates hired through external recruitment resources come from varied backgrounds. This helps them drive innovation and run things a little differently, thus bringing in a fresh approach to any project they undertake.
Companies cannot hire anyone to fill critical roles that require highly qualified executives. This task is assigned to executive headhunters who specialize in identifying and screening high-calibre candidates with the right amount of industry experience. Huge conglomerates and companies seek special talent through external recruiters who have carved a niche for themselves.
Now that you have learnt the different ways in which leveraging external sources of recruitment benefits companies, let’s take a look at some of the best practices of external recruitment to understand how to effectively use their resources.
Identifying, reaching out to and screening the right candidates requires a robust working system. Every system works efficiently if a few best practices are implemented. For example, hiring through social media platforms requires companies to provide details about their working environment, how the job is relevant to their audience and well-positioned advertisements. The same applies to the other external sources of recruitment. Here is how you can optimise the system to ensure an effective recruitment process.
Detail Responsibilities: Clearly outline the key responsibilities and expectations for the role.
Highlight Company Culture: Include information about the company’s mission, values, and growth opportunities to attract candidates who align with your organizational culture.
Diversify Sources: Use a mix of job boards, social media platforms, recruitment agencies, and networking events to maximize reach. Relying on a single source can limit your candidate pool.
Utilize Industry-Specific Platforms: In addition to general job boards, consider niche job sites that cater to specific industries or skill sets
Simplify Applications: Ensure that the application process is user-friendly. Lengthy or complicated forms can deter potential candidates from applying.
Mobile Optimization: Many candidates use mobile devices to apply for jobs, so ensure your application process is mobile-friendly.
Reach Out to Passive Candidates: Actively seek out candidates who may not be actively looking for a job but could be a great fit for your organization. Use LinkedIn and other professional networks for this purpose.
Maintain a Talent Pool: Keep a database of previous applicants and strong candidates for future openings, allowing you to reach out when new roles become available.
Promote Job Openings: Use social media platforms like LinkedIn, Facebook, and Twitter to share job postings and engage with potential candidates. This approach can also enhance your employer brand
Conduct Background Checks: There are several ways of learning about potential candidates. Checking out candidate profiles on job boards like LinkedIn or social media platforms can give companies a better understanding of their potential candidates, thus confirming whether they are the right fit for the organization.
Analyze Recruitment Metrics: Track key metrics such as time-to-hire, cost-per-hire, and source effectiveness. This data can help refine your recruitment strategies over time. Using external hiring software like HackeEarth can streamline the recruitment process, thus ensuring quality hires without having to indulge internal resources for the same.
Use Predictive Analytics: In this age of fast paced internet, everybody makes data-driven decisions. Using predictive analytics to study employee data will help companies predict future trends, thus facilitating a productive hiring process.
External sources of recruitment play a very important role in an organization’s talent acquisition strategy. By employing various channels of recruitment such as social media, employee referrals and campus recruitment drives, companies can effectively carry out their hiring processes. AI-based recruitment management systems also help in the process. Implementing best practices in external recruitment will enable organizations to enhance their hiring processes effectively while meeting their strategic goals.
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