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Charles Babbage's computer - History of computer programming- Part 1

Charles Babbage's computer - History of computer programming- Part 1

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Arpit Mishra
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February 28, 2017
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3 min read
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“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.

Charles Babbage – The father of the computer

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.

Difference Engine

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.

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How Does Charles Babbage’s Difference Engine work?

Wikipedia says: “A difference engine is an automatic mechanical calculator designed to tabulate polynomial functions.

The name derives from the method of divided differences, a way to interpolate or tabulate functions by using a small set of polynomial coefficients.”

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.

Difference 1 : D11 = R2 (Step 2) – R1 (Step 1) or D12 (Step 2) = R3 (Step 3) – R2 ( Step 2) 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

Difference 2 : D21 = D12 (Difference 1 -Step 2) – D11( Difference 1- Step 1), and so on.

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.

Working of a difference engine

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

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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.

Punch Cards

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.

Working

In the most basic form, a weaving design is made by passing onethread over another.

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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.

Ada Lovelace – The first programmer

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.

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Part 1 – The Store, was what we now call Hard disk or memory

Part 2 – The Mill, was what we now call Central Processing Unit (Mill where the churning or production is done)

Part 3 – Steam engine, which would be the source of energy

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.)

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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!

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February 28, 2017
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3 min read
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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:

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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.

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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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