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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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10 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.

History of Computer Programming - Lovelace, Babbage and Engines, Ada lovelace, history of computer programming, Charles Babbage, Difference engine detail, analytical engine, how does difference engine works, working of difference engine, Ada Lovelace worlds first programmer, worlds first programmer, world's first computer, Father of computer,

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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Arpit Mishra
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February 28, 2017
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10 min read
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A New Era of Code

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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 code.

The Promise and the Pitfalls

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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

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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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Guide to Conducting Successful System Design Interviews in 2025

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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

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

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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
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Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
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  • 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
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Evaluation Rubric for Candidates

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Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

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As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

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.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
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    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
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  4. Using remote desktop software and virtual machines


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    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
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