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.
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 code.
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 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.
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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.
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.
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.
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.
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:
Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.
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.
The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "
This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.
