Linux, 24 and dominant

To the uninitiated, the word Linux is synonymous with open source. This is because Linux, an open source OS had rose to prominence. This is marked as one of the most important event of the free and open source revolution. OSes built on the Linux kernel run a huge chunk of today’s computing world.

It’s been 24 years since Linux kernel first emerged in a Minix newsgroup on Usenet. It popped as a post from a 21 year old programmer from Finland, who went by the name Linus Torvalds. He was building a “free operating system” which was just to be a “hobby”. According to him the OS he built wouldn’t be “big and professional like GNU”.

Today the Linux kernel has over 19.5 million lines of source code, powers 97% of all supercomputers in the world. Among the innumerate applications that use Linux, nuclear submarines controls, air traffic control systems, the Japanese bullet train operation are a few. Linux also powers over a billion smartphones and embedded devices.

But what made it so popular? How did the Linux kernel achieve such large adoption? To answer this question, you must research the history of Linux. Somewhere along the way, you will stumble upon a document, called the Cathedral and the Bazaar. This document, written by Eric Raymond, explores the reasons behind the rapid and widespread development and the subsequent adoption of the Linux kernel.

The Cathedral is a philosophy of building software. Before the explosive growth of Linux, Eric Raymond believed that important software needs to be built like cathedrals -

“carefully crafted by individual wizards or small bands of mages working in splendid isolation, with no beta to be released before its time.”

Linux on the other hand, resembled something that came out of a loud, noisy and an unorganised bazaar. Linux opened its doors to anyone who wanted to contribute to the project. Everyone came with a different approach and agendas, and they came in huge numbers. How could a coherent and stable system ever emerge from such an environment?

Surprisingly, it did, and in an emphatic fashion. Linus Torvalds had built the largest community of kernel developers in the world. He wisely used his users as beta testers and ardently listened to them. He implemented user feedback rapidly and adhered to absurd release cycles (sometimes, 2 kernel releases a day!) and cleverly delegated ironing out the consequent issues and bugs to the several thousand co-developers. This stems into the infamous Linus law -

“Given enough eyeballs, all bugs are shallow”

This up, up and away trajectory of the adoption of the Linux kernel in the last 24 years have been as a result of a few key factors -

An able founder

While Linus Torvalds' true innovation lies in community building, motivation and resource marshalling. Building a production quality OS kernel from scratch is no easy job. Even to this day, Linus has the most number of commits to the Linux project.

This allowed Linus to position himself as a universally accepted gatekeeper of for the Linux project, where he could give the project direction, and exercise his legendary community building and management skills.

Intel’s proliferation into the PC and server market

In the beginning, many of the Linux kernel’s functionalities, like task switching, used exclusive features of the Intel 386 processor. This meant that the Linux kernel couldn’t be used on any other piece of hardware. This was one of the arguments Andrew Tanenbaum, the creator of Minix, used when he called Linux obsolete

While the open source development model ensured that the Linux kernel was ported to all other major hardware platforms, the Intel x86 series of processors soon became widely used by PCs and servers, ensuring the relevance of the Linux kernel.

The Internet

By 1994, Linux had stepped out of its infancy and the Linux developer community had begun to leverage the internet to make the world its talent pool. This time period also saw the growth of the ISP industry and that gave rise to large scale adoption of the internet.

The internet became cheaper and easier to access and more developers were online, which helped the community grow. But more importantly, it gave Linus a vast and effective communication and collaboration channel.

Channeling egos

Today, 80% of Linux kernel developers are paid. However, in the early days of Linux, the story was very different. They were mostly volunteers. And volunteer motivation relies mainly on satisfying their egos.

Linus nurtured enough interest in the Linux project that hackers got an ego boost by contributing to it. Furthermore, he connected the developers’ selfish motives and channeled it to result in sustained corporation, that ensured rapid and cohesive growth of the project.

“A good hack is a solution to a personal problem”

While the open source method is a great way to build and develop software, it might not be the best way to create one. There must be a project founder, who floats an idea to a developer community. The community must find the hack interesting and believe that it has potential to be something great. From there on, the founder must continue to nurture interest in it, till it reaches a point of self sustenance.

At the end of the day, it’s all about the hack.

By 1991, there were quite a few endeavors towards making a free and open source operating system. Richard Stallman was working on the GNU project, but the Kernel that powered it, HURD, failed to attract interest from other developers. UC Berkeley built the Berklee software distribution or BSD, which was built from UNIX. But, BSD contained UNIX code that was owned by AT&T, and the company filed a lawsuit against the University of California for it. And then there was Tanenbaum’s MINIX OS - its source code was available, but you couldn’t modify and redistribute it.

All this, made a 21 year old in Finland want his own, free and open source operating system. And that, is how Linux came into existence.

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

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

Introducing a New Era in Mobile Assessment

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

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

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

Assess the Skills That Truly Matter

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

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

Streamlining Your Assessment Workflow

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

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

Quantifiable Impact on Hiring Success

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

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Focus purely on showcasing skills, not environment configurations.
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Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

A New Era of Code

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

From Machine Language to Natural Language

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

The Promise and the Pitfalls

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

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

The Economic Impact

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

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

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

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

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

Develop an understanding of basics of system design and architecture
Familiarize yourself with commonly asked systems design interview questions
Read about system design case studies for popular applications
Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

Plan the extent of the topics and scope of discussion in advance
Clearly define the evaluation criteria and communicate expectations
Quantify constraints, inputs, boundaries and assumptions
Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

Ask follow-up questions to challenge a solution
Probe candidates to gauge real-time logical reasoning skills
Make it a conversation and take notes of important pointers and outcomes
Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

Encourage candidates to explore and consider alternative solutions
Work with the candidate to drill the problem into smaller tasks
Provide context and supporting details to help candidates stay on track
Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.