Key Takeaways:

GPS navigation represents streets as graphs with nodes and edges instead of single long roads.
Breadth First Search (BFS) explores all neighboring nodes level by level to find the shortest path.
A grid example shows BFS visiting nodes step by step until it reaches the destination.
BFS can find the shortest path in terms of distance, but real GPS also considers traffic, speed limits, and signals.
Modern GPS systems often use the A* algorithm, which is more efficient than BFS for complex maps.

There are differences in the route which I usually take and the one which GPS shows as the shortest, probably due to the algorithms used. I learned from my graph theory data structure classes that (BFS) Breadth First search example is GPS navigation and digital maps. I tried looking for the possible use of Algorithms (Breadth First Search example or A* application) used in GPS navigation on the web, but I couldn’t find a lot of details. So here is how Breadth First Search is used in real life application like GPS.

Let’s first understand working of GPS navigation

Digital maps, unlike humans, see streets as a bunch of nodes. The 2.6-mile road from the Columbus Circle station (59 st) to Cathedral Pkwy (110 st) is called Central Park West. We (humans) consider this road a single entity (You may divide it into few more segments based on metro stations or intersections, but not more than that).

Central Park West Map

But a GPS navigation or any other digital map divides it into hundreds of segments, with some only 24 meters long. A GPS looks at this street as a graph divided into vertices and edges.

Graph Representation of Streets

Considering this, there is a lot of data to be covered and calculated while finding the shortest path.

What is a graph?

A graph usually looks like the image below and is made up of vertices and edges (represented by lines and circles, respectively).

Graph Nodes and Edges

The objective of a graph is to represent a problem as a set of points that are connected in various ways using edges. With the help of such graphs, we tend to solve our problems by applying various algorithms.

Let’s take an example to understand better.

Facebook is a good example to understand graph theory.

Facebook has millions of users. If a person needs to find a friend, he can use an array and search. But that would take a lot of time and memory to search for so many people, making the problem quite complex.

But if the same scenario is represented using a graph, the problems tend to get solved easily. With a graph, you know that these two people are actually friends (Though real-life scenarios are not exactly that simple!). Check this video on how graph theory is used in social networks:

Graph theories are frequently used in various other fields, such as maps, e-commerce, and computer games.

Before we go further down this road, read this detailed article about graph theory, which explains other important aspects of Graphs such as Directed, Undirected, Cycle or Loop, and Matrix.

What’s the difference between a Graph and a Tree?

A tree is a special type of graph, i.e., a minimal graph, where there is only one path between two vertices.

So what is Breadth First Search and how does it work?

Depth First Search (DFS) and Breadth First Search (BFS) are algorithms, or in simple terms, they are methods to traverse a graph.

Before I explain Breadth First Search, consider this example.

Take a graph with 13 nodes. When Breadth First Search is applied to this graph, the algorithm traverses from node 1 to node 2 and then to nodes 3, 4, 5, 6 (in green) and so on in the given order.

If you consider 1 (in red) as the first node, you observe that Breadth First Search gradually moves outward, considering each neighboring node first.

BFS Example on Graph

This eventually brings us to the accepted definition of the Breadth First Search algorithm:

“Breadth First search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a "search key") and explores the neighbor nodes first, before moving to the next level neighbors.”

Graph Traversal in Maps

Take a look at this simple “Gridworld” which is used for various graph traversal algorithms. Your digital map considers your world a similar grid, which is made up of intersections connected to each other.

Grid World

Now for the grid shown, there could be N number of ways to traverse from point A to point P.

Following are two of these N ways in which one can travel from point A to point P.

Multiple Gridworld Paths

So how does an algorithm decide which the shortest way to reach a destination is? Graph Traversal Algorithms!

The Breadth First Search algorithm looks at the map as we do; it just can’t perceive it completely. When you have to travel from one destination to another, you draw a line from point A to point B, and then chose the road closest to that line. Algorithms repeat the same method choosing the node nearest to the intersection points, eventually selecting the route with the shortest length.

Let’s take a simple example of GridWorld given above and try solving it using Breadth First Search. Assume you need to travel from location A to location P.

Note: Every vertex in the image is given a number, which is the total distance from the source and an alphabet which represents the previous node.

Breadth First Search for GridWorld

Step 1 - Visit neighboring nodes to A, i.e, B, E, and F. The vertex to B would become 1-A and since E and F are also at an equal distance as B, hence vertices to both E and F from A, could be denoted as 1-A too.

BFS Step 1

Step 2 - Mark "A" as visited. Use B as the source node. Visit adjacent nodes to B: C (2B) and G (2B). Node F is already considered.

BFS Step 2

Step 3 - Visit neighboring nodes of E: I (2E) and J (2E), and mark E as visited.

Step 4 - Visit neighbors of F: K (2F). F is marked as visited.

Step 5 - Repeat until all nodes are visited.

Step 6 - The shortest route from A to P is diagonal with distance 3.

Shortest BFS Path

Removing unused vertices creates a minimum spanning tree, where each node is connected to at least one vertex.

But in real scenarios, diagonal movement isn't always possible. Let's analyze GridWorld again, this time disallowing diagonal moves.

Step 1 - Source node A: visit B(1A), E (1A). Mark A as visited.

BFS No Diagonal Step 1

Step 2 - Node B: visit C (2B) and F (2B), mark B as visited.

Step 3 - Node E: visit I (2E), mark E as visited.

BFS No Diagonal Step 3

Step 4 - Continue visiting all nodes and marking visited.

Step 5 - Remove unconnected vertices, and build the minimum spanning tree.

Step 6 - Highlight shortest path A to P with a distance of 6.

Shortest BFS Path Without Diagonal

You now understand why GPS navigation didn't suggest the path A, E, I, M, N, O, P or A,B,C, D, H, L, P though they were equidistant.

Once you've understood the way GPS works, you’d wish the world could be a simple Grid! But to a programmer's disappointment, it isn’t. Hence, for a GPS, distance is not the only factor in choosing a route, rather elapsed time, the speed limit on a route, live traffic update, the number of stop signals all has to be taken into consideration. That’s why you would find your GPS occasionally suggesting winding state highways to travel instead of the usual national highways.

Most of the GPS or digital maps have evolved over Breadth First Search to A* algorithm (You can read more about A* algorithm - Here) due to better complexity over a period of time.

Yet, GPS is one of the most amazing devices. Connected to satellites 12,000 miles above the planet, it calculates your position in real time with more than 50,00,000 possibilities for a particular route.

Watch the video explaining the Use of Breadth first search in GPS navigation here:

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

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My Learning Journey

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

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

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

But was it actually good code?

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

What I Thought Was "Good Code"

A working image carousel with:

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

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

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

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

What VibeCodeArena's Evaluation Showed

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

Security Vulnerabilities (The Scary Ones)

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

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

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

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

Performance Problems (The Silent Killers)

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

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

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

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

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

Code Quality Issues (The Technical Debt)

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

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

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

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

Missing Best Practices (The Professional Touches)

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

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

The "Aha" Moment

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

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

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

My New Workflow: The Learning Loop

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

Step 1: Generate Code Using VibeCodeArena

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

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

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

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

Step 3: Click "Challenge" and Improve

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

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

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

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

Step 4: Submit for Evaluation

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

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

Step 5: Hey, I Can Beat AI

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

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

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

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

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What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

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

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

2. Improvement Requires Measurement

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

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

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

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

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

How the Platform Helps Me Become A Better Programmer

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

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

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

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

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

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

What I've Learned So Far

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

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

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

The Bottom Line

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

VibeCodeArena bridges that gap by providing:

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

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

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

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

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

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Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

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

Introducing a New Era in Mobile Assessment

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

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

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

Assess the Skills That Truly Matter

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

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

Streamlining Your Assessment Workflow

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

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

Quantifiable Impact on Hiring Success

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

A Better Experience for Everyone

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

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Accurately assess real-world development skills.
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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 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.

Breadth First Search example (BFS) - How GPS navigation works

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