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List of top C & C++ books for programming enthusiasts

List of top C & C++ books for programming enthusiasts

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Arpit Mishra
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March 30, 2017
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3 min read
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Perhaps a post on these programming languages needs no fore ward. But then again, for the skeptics who are rooting for Go and Swift, here’s a little bit of background that reinforces the fact that despite not being the most popular ones today, these object-oriented languages still form the base for many applications.

Why bother

Java and C# were touted as the pet languages of the 2000s. Now, people talk Python and Ruby, Javascript and PHP.However, fundamental programming skills still necessitate a solid foundation in C and C++. (You can read more here- Top programming languages that will be most popular in 2017)

TIOBE may be scorning C now, but Dice and other job portals show a significant demand for these skill sets across industries. Beginner-friendliness, scalability, and a sizable community continue to make C++ a major player as well.

“They [C and C++] are the native tongue for system-level programming, and they probably will be for many years. Eventually, though, languages like Google Go or D may replace them,” says Gartner Research Analysts Mark Driver. “The trial-by-fire of learning C tends to weed out the noncommitted, so knowledge of C at the very least makes you stand out,” he added.

These languages act like a “mental model” that helps you go where places you thought you couldn’t. Bjarne Stroustrup, the C++ creator, says, “Basically, nothing that can handle complexity runs as fast as C++.” Used with some scripting language, it is for “high performance, high reliability, small footprint, low energy consumption, all of these good things.”

With a plethora of resources available, choosing the best can leave you in a tizzy. We’ve got a list, a valuable one, which keeps the curious ones who wonder what’s beneath the hood get as “close to the machine” as possible.

List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Stroustrup: The C++ Programming Language (4th Edition)

What’s better than studying from the guru himself? Bjarne Stroustrup created C++ in 1979.

The book covers the language in its entirety, talking about containers, algorithms, abstraction mechanisms, concurrency, utilities, basic facilities, standard libraries, and design models. This reorganized edition discusses C++11, a version that followed C++03, and then got superseded by C++14 and C++17 later on. A must-have for programming enthusiasts, because it certainly is a definitive reference book for general programming principles and practice using C++. Reviewers are raving about the code examples and the way the language has been presented. It may not be the best book for novices according to some readers; it is more of a “description of the features and the reasoning” than answering how-tos. Look at the detailed table of contents here and access the exercises here.

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Accelerated C++: Practical Programming by Example by Andrew Koenig and Barbara E. Moo

For learners who are eager to get into the practical aspects of C++, this book, which is a part of Stroustrup’s C++ in-depth series, is the go-to reference. If you don’t have time for the basics, then you can go directly into the coding bit with the help of Koenig and Moo’s “accelerated” C++. Topics covered include “basic string handling, loop and flow-control statements, arrays, functions and methods, iterators, file I/O, operator overloading, inheritance, polymorphism and virtual functions.”

Founding member of the ANSI/ISO C++ committee, Dag Brück, says “This is a first-rate introductory book that takes a practical approach to solving problems using C++. It covers a much wider scope of C++ programming than other introductory books I’ve seen, and in a surprisingly compact format.” The authors talk about features using understandable examples, teaching you how to use the features rather than trying to explain the whats and whys. It takes you from standard library abstractions to defining your own. Key takeaways that crystallize low-level and high-level concepts and end-of-chapter exercises cement your understanding.

With this book, you can begin programming right away!

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

C++ Primer (5th Edition) 5th Edition by Stanley B. Lippman, Josée Lajoie, and Barbara E. Moo

In the C++ primer, the authors focus on the 2011 revised standard. In the Why Read This Book section, they say they “emphasize good style and explain the rationale behind the rules.” The first part of the book covers basics of C++ such as variables, strings, vectors, arrays, expressions, statements, functions, and classes. The next section deals with the I/O library, sequential and associative containers, generic algorithms, and dynamic memory. Another part takes you through copy control, overloaded operations and conversions, OOP, templates, and generic programming. The primer teaches you high-level programming techniques, such as specialized library facilities and tools for large programs, in the later sections. Learners don’t have to know C, but they need to be familiar with writing, compiling, and running a program “in at least one modern block-structured language.”

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Effective Modern C++: 42 Specific Ways to Improve Your Use of C++11 and C++14 (1st Edition) by Scott Meyers

A part of his Effective C++ book series, this edition talks about how you can use the new features of C++11 and C++14, such as lambda expressions and move semantics, effectively. A software architect at Microsoft and chair of ISO C++ standards committee, Herb Sutter, says: “After I learned the C++ basics, I then learned how to use C++ in production code from Meyer’s series of Effective C++ books. Effective Modern C++ is the most important how-to book for advice on key guidelines, styles, and idioms to use modern C++ effectively and well. Don’t own it yet? Buy this one. Now.”

With this book, Meyers ensures that you can “create software that’s correct, efficient, maintainable, and portable.” Topics covered include perfect forwarding, except specifications, braced initialization, auto type declarations, and differences between std:: atomic and volatile and their relation to the concurrency API of C++.

A few reviewers feel that some basic knowledge of C++ is required to fully appreciate this edition on modern C++. Lots of great examples and bite-sized “items” tell you why the features have been added and what they can do; it is a set of guidelines on the newer additions to C++ rather than an introductory text to learn C++.

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Exceptional C++: 40 New Engineering Puzzles, Programming Problems, and Solutions by Herb Sutter

This top-quality book is a part of Stroustrup’s C++ in-depth series. Written by Herb Sutter, arenowned expert in C++, the book talks about the what, the why, and the how-to of “solid software engineering” using scenarios in a problem-solution format. Sutter answers questions such as “How does writing inline affect performance? How does exception safety go beyond try and catch statements? What’s the real memory cost of using standard containers?”

If you want to be one of the best C++ programmers around, Exceptional C++ is a definitive guide to topics such as generic programming, writing reusable templates, exception safety issues, compiler firewalls, class design, inheritance, and polymorphism, and optimization. Exemplary presentation and entertaining puzzles make this a must-buy. His next book, More Exceptional C++: 40 New Engineering Puzzles, Programming Problems, and Solutions continues the journey. With an aim to help you write exceptional code, the book comes with new detailed sections (e.g. multi-threaded environments) and insights on vital topics covered in the prequel.

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

The C Programming Language 2nd Edition by Brian W. Kernighan and Dennis M. Ritchie

Despite having been originally published in 1978, this amazing book continues to be the bible for C programmers. Ritchie (1941–2011) was the original C language designer, and he also co-designed the UNIX OS. The K&R (authors) C version is different from the ANSI C or the earlier version.

The book discusses has challenging exercises to help you attain a working knowledge of C. It concisely and clearly types, operators, and expressions, control flow, functions and program structure, pointers and arrays, structures, input and output, and the UNIX system interface. You need some programming background; you need to know what a compiler is; the book teaches you the syntax and not exactly the programming principles. For example, when it talks four pages about functions, it doesn’t actually tell you what a function is. Still, this seminal text has the first Hello World program.

In the preface to the second edition published in 1988, the authors write: We have improved the exposition of critical features, such as pointers, that are central to C programming. We have refined the original examples, and have added new examples in several chapters. For instance, the treatment of complicated declarations is augmented by programs that convert declarations into words and vice versa. As before, all examples have been tested directly from the text, which is in machine-readable form.

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

A Book on C: Programming in C (4th Edition) by Al Kelley and Ira Pohl

Kelley and Pohl have put together a great tutorial on ANSI C. The authors have used unique and clear explanations of program code, along with all-encompassing exercises and summary tables, to highlight the power of C, a general purpose programming language.

The USPs of the book include a chapter on how to move to Java from C, detailed coverage of pointers, multi-file programming, and recursion, an improved standard library functions appendix, and more focus on abstract data types. The comprehensive tutorial on ANSI C also discusses input/output and the operating system, lexical elements, operators, and the c system, the preprocessor, structures, functions, unions, transitioning to C++ from C, how ANSI C is different from traditional C, and advanced applications.

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Expert C Programming: Deep C Secrets by Peter van der Linden

This book isn’t for a beginner either. Once you have learned C from K & R, Linden’s book can answer questions such as “How can you debug linker errors? What is an activation record? Why are arrays and pointers not identical?” Unlike most bland technical books, Linden has managed to keep the reader engaged with humor, puzzles, depth of content, cultural references, and exercises. Although some bits in the book may not seem relevant anymore, it is still a satisfying read with its hacker stories and more.

John Barry, the author of Sunburst, Technobabble, and other books says “In Expert C Programming, Peter van der Linden combines C language expertise and a subtle sense of humor to deliver a C programming book that stands out from the pack. In a genre too often known for windy, lifeless prose, van der Linden’s crisp language, tongue-in-cheek attitude, and real-world examples engage and instruct.”

For C programming enthusiasts, this book is about the background stories and the appreciation for the language. The lore aside, Linden discusses advanced concepts related to compiling, pointers, and memory usage. The 11 chapters have positive titles that make you curious about linking, runtime data structures, declarations, arrays, and so on.

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Let us C by Yashavant P. Kanetkar

This is a book that helps you learn C from scratch. The author, who says he picked up the language from Dennis Ritchie’s book on C programming, has explained the basic concepts such as decision control instruction, complex decision making, loop control instruction, complex repetitions, case-control instruction, functions, pointers, recursion, data types revisited, the c preprocessor, arrays, strings, structures, console input/ output and file input/ output, C in Linux, and operations on bits in an easy-to-understand format. The book also teaches you how to create programs using Visual Studio and NetBeans.

You can buy it here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Introduction to Algorithms 3rd Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

This is what Daniel Spielman, Henry Ford II Professor of Computer Science, Mathematics, and Applied Science at Yale, has to say about this book, Introduction to Algorithms, the ‘bible’ of the field, is a comprehensive textbook covering the full spectrum of modern algorithms: from the fastest algorithms and data structures to polynomial-time algorithms for seemingly intractable problems, from classical algorithms in graph theory to special algorithms for string matching, computational geometry, and number theory. The revised third edition notably adds a chapter on van Emde Boas trees, one of the most useful data structures, and on multithreaded algorithms, a topic of increasing importance.”

The book is meant for readers at all levels. With a bit of programming background, learners can grasp the magic—design, and analysis—of algorithms. The book broadly covers foundations, sorting and order statistics, data structures, advanced techniques such as dynamic programming and greedy algorithms, advanced data structures such as Fibonacci Heaps and van Emde Boas Trees, graph algorithms, and a few selected topics such as matrix operators, linear programming, polynomials and FFT, string matching, computational geometry, and NP-completeness.

You can buy the book here.


List of top C & C++ books for programming enthusiasts,What are the best C++ books, best C books for beginners

Data Structures and Algorithms Made Easy by Narasimha Karumanchi

For whom is this book? Prof. Hsin-Mu Tsai, National Taiwan University, answers it in his book review. He says, “This book is a good supplement to a conventional data structure textbook, as it offers many good code examples and selections of relevant problems **with solutions**. There is no deep analysis or detailed proof in this book, which is not what this book is for (for example, as a textbook to teach algorithm and complexity analysis), and what you would be able to find in a conventional data structure textbook. The book could also be good for a professional who just wants a quick review of important data structure concepts and implementations.”

Reviewers on Amazon believe that this book is a must-have for job interviews and competitive exams. The author emphasizes problem analysis over theory. The book is coded in C and C++. A comprehensive introduction, recursion and backtracking, linked lists, stacks, queues, trees, heaps, graph algorithms, sorting, searching, selection algorithms, symbol tables, hashing, string, divide-and-conquer, and greedy algorithms, complexity classes, and dynamic programming are the key chapters in the book. Looks like he has covered just about everything you need for a binge-reading evening!

You can buy the book here.


Summary

Computers are not about calculations, they are about information—organizing, retrieving, and manipulating it. You want to write efficient programs? Then you need to understand and learn to work with data structures. Data structures and algorithms tell you how you can put the programming languages you mastered to good use. Pick up C and C++ and implement and play around with data structures, and see how exciting it all is. In spite of young upstarts, dependable C and C++ continue to be the programming languages of choice for several applications.

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March 30, 2017
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How I used VibeCode Arena platform to build code using AI and leant how to improve it

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

My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

  • Clean, semantic HTML
  • Smooth CSS transitions
  • Keyboard navigation support
  • ARIA labels for accessibility
  • Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

  • No DNS-prefetch hints for external image domains
  • Missing width/height attributes causing layout shift
  • No preload directives for critical resources
  • Missing CSS containment properties
  • No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

  • No 'use strict' directive in JavaScript
  • Missing package.json for dependency management
  • No test files
  • Missing README documentation
  • No .gitignore or version control setup
  • Could use functional array methods for cleaner code
  • Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

  • Security vulnerabilities
  • Performance/Efficiency issues
  • Performance optimization opportunities
  • Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

  • What actually improved (and by how much)
  • What new issues I might have introduced
  • Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

  • How to implement Content Security Policy correctly
  • Why DOM depth matters for rendering performance
  • What CSS containment does and when to use it
  • How to structure code for better maintainability
  • Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

The Mobile Dev Hiring Landscape Just Changed

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Article content

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Article content

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin
This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way
Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

For Recruiters & Hiring Managers:

  • Accurately assess real-world development skills.
  • Gain deeper insights into candidate proficiency.
  • Hire with greater confidence and speed.
  • Reduce candidate drop-off from technical friction.

For Candidates:

  • Enjoy a seamless, efficient assessment experience.
  • No need to switch between different tools or manage complex setups.
  • Focus purely on showcasing skills, not environment configurations.
  • Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

Vibe Coding: Shaping the Future of Software

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

Vibe Coding Difference

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable or Hostinger Horizons enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

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