DEV Community: Aman Verma

JAVA ARRAY NOTES_day-2

Aman Verma — Wed, 25 Feb 2026 02:49:14 +0000

best Program practise in arrays
Q6) WAP to find the Maximum Element in the Array

public class MaxElement {
public static void main(String[] args) {

    int[] arr = {12, 45, 7, 89, 34};

    int max = arr[0];

    for(int num : arr) {
        if(num > max) {
            max = num;
        }
    }

    System.out.println("Maximum Element = " + max);
}

}

✔ Compare each element
✔ Store the largest value

Q7) HEAP and STACK Diagram

Array objects are created in the heap memory.

Example:

int[] arr = new int[5];
HEAP MEMORY

1000x: ArrayObject [0, 0, 0, 0, 0]

STACK MEMORY

arr → 1000x

✔ Stack → Reference variable

✔ Heap → Actual Array Object

Q8) Modify Array Element

import java.util.Arrays;

class ArrayElementModification {

    public static int[] modifyElement(int[] arr) {

        arr[0] = 100;
        arr[1] = 200;
        arr[2] = 300;

        return arr;
    }
}

public class ArrayExDemo8 {

    public static void main(String[] args) {

        int[] val = {10,20,30,40,50};

        System.out.println("Original Array : " + Arrays.toString(val));

        int[] modified = ArrayElementModification.modifyElement(val);

        System.out.println("After Modification : " + Arrays.toString(modified));
        System.out.println("Original Array is : " + Arrays.toString(val));
    }
}

✔ Arrays are passed by reference
✔ Changes reflect in othe riginal array

--------------------------------------------------------
Q9) Bubble Sort (Ascending Order)
--------------------------------------------------------

java

import java.util.Arrays;

public class BubbleSortDemo {

public static void main(String[] args) {

    int[] arr = {20, 10, 30, 40, 50, 90};

    for(int i = 0; i < arr.length; i++) {
        for(int j = i + 1; j < arr.length; j++) {

            if(arr[i] > arr[j]) {
                int temp = arr[i];
                arr[i] = arr[j];
                arr[j] = temp;
            }
        }
    }

    System.out.println("Sorted Array : " + Arrays.toString(arr));
}

}

✔ Time Complexity → O(n²)

Q10) Search Element (Linear Search)


public class SearchElement {

    public static void main(String[] args) {

        int[] arr = {20,30,10,50,90,30};
        int target = 30;

        for(int i = 0; i < arr.length; i++) {
            if(arr[i] == target) {
                System.out.println("Element found at index : " + i);
            }
        }
    }
}

✔ Linear Search → O(n)

--------------------------------------------------------
Q11) Find Duplicate Elements
--------------------------------------------------------

java

public class DuplicateDemo {

public static void main(String[] args) {

    int[] arr = {2,3,9,2,8,7,3,2};

    for(int i = 0; i < arr.length; i++) {
        for(int j = i + 1; j < arr.length; j++) {

            if(arr[i] == arr[j]) {
                System.out.println("Duplicate Element : " + arr[i]);
            }
        }
    }
}

}

✔ Nested Loop → O(n²)

Q11) Remove Duplicate Elements



public class RemoveDuplicate {

    public static void main(String[] args) {

        int[] arr = {2,3,9,2,8,7,3,2};

        for(int i = 0; i < arr.length; i++) {
            for(int j = i + 1; j < arr.length; j++) {
                if(arr[i] == arr[j]) {
                    arr[j] = -1;
                }
            }
        }

        for(int num : arr) {
            if(num != -1) {
                System.out.print(num + " ");
            }
        }
    }
}
--------------------------------------------------------
Q12) Insert Element at Last Position
--------------------------------------------------------

java

import java.util.Arrays;

class InsertAtLast {

public static int[] insertLast(int[] arr, int element) {

    int[] newArr = new int[arr.length + 1];

    for(int i = 0; i < arr.length; i++) {
        newArr[i] = arr[i];
    }

    newArr[newArr.length - 1] = element;

    return newArr;
}

}

Q13) Insert Element at Middle Position


java

class InsertAtMiddle {

    public static int[] insertMiddle(int[] arr, int element) {

        int[] newArr = new int[arr.length + 1];
        int middle = arr.length / 2;

        for(int i = 0; i < middle; i++) {
            newArr[i] = arr[i];
        }

        newArr[middle] = element;

        for(int i = middle; i < arr.length; i++) {
            newArr[i + 1] = arr[i];
        }

        return newArr;
    }
}
Q14) Merge Two Arrays
import java.util.Arrays;

class Merge {

    public static int[] mergeArrays(int[] x, int[] y) {

        int[] newArr = new int[x.length + y.length];
        int position = 0;

        for(int element : x) {
            newArr[position++] = element;
        }

        for(int element : y) {
            newArr[position++] = element;
        }

        return newArr;
    }
}


--------------------------------------------------------
                🔥 FINAL SUMMARY 🔥
========================================================

✔ Max → Compare and update
✔ Modify → Arrays are reference type
✔ Sort → Nested loop
✔ Search → Linear scan
✔ Duplicate → Compare pairs
✔ Insert → Create new array
✔ Merge → Create bigger array

JAVA ARRAY NOTES_day-1

Aman Verma — Wed, 25 Feb 2026 02:46:33 +0000

==================================================

🔷 JAVA ARRAY NOTES 🔷

1) WHY ARRAY?

Before Array:
int a = 10;
int b = 20;
int c = 30;

Problem:

Too many variables
No sequential memory
Difficult to manage
Hard to use loops

Solution → ARRAY

2) WHAT IS AAN RRAY?

An array is a data structure that stores
multiple values of the same data type
in contiguous (sequential) memory location.

Syntax:

datatype[] arrayName;

Example:
int[] arr;

Memory Allocation:
arr = new int[5];

int[] arr = new int[5];

3) MEMORY REPRESENTATION

int[] arr = {10,20,30,40};

Index Value

0 10
1 20
2 30
3 40

✔ Index starts from 0
✔ Last index = length - 1
✔ Stored in Heap memory
✔ Continuous memory allocation

4) CHARACTERISTICS OF ARRAY

Homogeneous
-> Same data type only.
Fixed Size
-> Size cannot be changed after creation.
Indexed-Based
-> arr[0], arr[1], arr[2] ...
Contiguous Memory
-> Elements stored next to each other.
Default Values
int -> 0
double -> 0.0
boolean -> false
object -> null
Fast Access
-> O(1) time complexity.
Length Property
-> arr. length (variable, not method)

5) TYPES OF ARRAYS

1)Single-Dimensionall Array
2) Multi-Dimensional Array

6) WAYS TO CREATE AN ARRAY

Method 1:
int[] arr = new int[5];

Method 2:
int[] arr = {10,20,30,40};

Method 3 (Anonymous Array):
int[] arr = new int[]{100,200,300};

7) PROGRAMS

1) SUM OF ARRAY

public class SumArray {
public static void main(String[] args) {

    int[] arr = {10,20,30,40};
    int sum = 0;

    for(int num : arr){
        sum = sum + num;
    }

    System.out.println("Sum = " + sum);
}

}

2) REVERSE ARRAY

public class ReverseArray {
public static void main(String[] args) {

    int[] arr = {10,20,30,40};

    for(int i = arr.length - 1; i >= 0; i--){
        System.out.print(arr[i] + " ");
    }
}

}

3) MINIMUM ELEMENT

public class MinArray {
public static void main(String[] args) {

    int[] arr = {12,5,78,3,9};
    int min = arr[0];

    for(int num : arr){
        if(num < min){
            min = num;
        }
    }

    System.out.println("Minimum = " + min);
}

}

8) DRAWBACKS OF ARRAY

1) Fixed Size
2) Insertion is costly
3) Deletion is costly
4) No built-in methods
5) Only the same type of elements

9) IMPORTANT EXAM POINTS

✔ Array is an object in Java.
✔ Stored in Heap memory.
✔ Size is fixed.
✔ Index starts from 0.
✔ Access time is O(1).
✔ Out of range → ArrayIndexOutOfBoundsException.

==================================================

🔥 FINAL SUMMARY 🔥

ARRAY =

Same type elements
Continuous memory
Fixed size
Index-based
Fast access

==================================================

If you want next:
1) 2D Array Notes (console style)
2) 20 Array Practice Programs
3) Array Interview Questions
4) Internal Working Diagram Explanation

Advanced java concept

Aman Verma — Sat, 21 Feb 2026 03:19:42 +0000

====================================================

JDBC – SERVLET – JSP NOTES

🔷 1. JDBC – ResultSet Interface

• ResultSet is an interface from java.sql package.
• Represents data returned by SQL SELECT query.
• Used for:
- Retrieving data
- Navigating rows
- Manipulating data

🔹 Creating ResultSet

Using Statement:

Statement stm = con.createStatement();
ResultSet rs = stm.executeQuery("SELECT * FROM emp");

Using PreparedStatement:

PreparedStatement ps = con.prepareStatement("SELECT * FROM emp");
ResultSet rs = ps.executeQuery();

🔷 2. Types of ResultSet

1) TYPE_FORWARD_ONLY (Default)
• Move only forward
• Cannot move backward
• Cannot jump to specific row

2) TYPE_SCROLL_INSENSITIVE
• Move forward & backward
• Jump to any row
• Does NOT reflect DB changes

3) TYPE_SCROLL_SENSITIVE
• Move forward & backward
• Reflects DB changes

🔷 3. Concurrency Mode

ResultSet.CONCUR_READ_ONLY
• Cannot update data

ResultSet.CONCUR_UPDATABLE
• Can update data

🔷 4. Important ResultSet Methods

next() → Move to next row
previous() → Move to previous row
first() → Move to first row
last() → Move to last row
beforeFirst() → Before first row
afterLast() → After last row
absolute(int) → Move to specific row
relative(int) → Move relative position

🔷 5. RowSet

• Child interface of ResultSet
• Supports Connected & Disconnected mode

Types:

JdbcRowSet
CachedRowSet
WebRowSet
FilteredRowSet
JoinRowSet

🔷 6. RowSetFactory

RowSetFactory rsf = RowSetProvider.newFactory();
CachedRowSet crs = rsf.createCachedRowSet();

🔷 7. CallableStatement (Stored Procedure)

• Used to execute:
- Stored Procedures
- Functions

CallableStatement cs =
con.prepareCall("{call proc_name(?)}");

Register OUT Parameter:

cs.registerOutParameter(1, Types.INTEGER);

Procedure vs Function

Procedure:
• No return value
• IN, OUT, INOUT
• Used for operations

Function:
• Returns value
• Only IN
• Used for calculations

🔷 8. Transaction Management

Transaction = Group of operations executed together

Steps:

1) Disable AutoCommit
con.setAutoCommit(false);

2) Execute Queries

3) Commit OR Rollback
con.commit();
con.rollback();

🔷 ACID Properties

Atomicity → All or nothing
Consistency → DB remains valid
Isolation → Transactions isolated
Durability → Changes permanent

🔷 9. Savepoint

Savepoint sp = con.setSavepoint();
con.rollback(sp);

🔷 10. Connection Pooling

• Improves performance
• Reuses DB connections
• Avoids repeated creation/destruction

🔷 11. JDBC Metadata

1) DatabaseMetaData

DatabaseMetaData dbmd = con.getMetaData();
dbmd.getDatabaseProductName();
dbmd.getDriverName();

2) ParameterMetaData

ParameterMetaData pmd = ps.getParameterMetaData();
pmd.getParameterCount();

3) ResultSetMetaData

ResultSetMetaData rsmd = rs.getMetaData();
rsmd.getColumnCount();
rsmd.getColumnName(1);

====================================================

SERVLET

🔷 12. What is Servlet?

• Java program running on server
• Handles HTTP requests
• Generates dynamic response

🔷 Servlet Life Cycle

1) init()
2) service()
3) destroy()

🔷 Servlet Interface Methods

public void init()
public void service()
public void destroy()
public ServletConfig getServletConfig()
public String getServletInfo()

🔷 13. Creating Servlet (3 Ways)

1) Implement Servlet
2) Extend GenericServlet
3) Extend HttpServlet (Most Used)

HttpServlet Methods:

doGet()
doPost()

🔷 14. RequestDispatcher

Used for:
• forward()
• include()

RequestDispatcher rd =
request.getRequestDispatcher("page.jsp");

rd.forward(request, response);

====================================================

JSP

🔷 15. JSP Scripting Tags

Scriptlet:

<% code %>

Expression:

<%= value %>

Declaration:

<%! int x = 10; %>

🔷 Directive Tags

Page:
<%@ page contentType="text/html" %>

Include:
<%@ include file="header.jsp" %>

Taglib:
<%@ taglib ... %>

🔷 Action Tag

====================================================

MVC ARCHITECTURE

🔷 16. MVC Components

Model:
• JavaBeans
• DAO
• Database

View:
• JSP
• HTML

Controller:
• Servlet

Flow:

HTML → Servlet → Bean → DAO → Database
Servlet → JSP (Display Result)

🔷 17. JavaBeans Rules

• Must implement Serializable
• Private variables
• Public getter & setter
• Public default constructor

🔷 18. Attributes in Servlet

request.setAttribute("name", value);
request.getAttribute("name");
request.removeAttribute("name");

🔷 19. GET vs POST

GET:
• Data in URL
• Less secure
• Limited size
• Used for retrieval

POST:
• Data in body
• More secure
• Large data allowed
• Used for submission

🔷 20. Session Tracking

Methods:
• Cookies
• HttpSession
• URL Rewriting
• Hidden Fields

🔷 21. Cookie Class

Cookie c = new Cookie("name","value");
c.setMaxAge(60);
response.addCookie(c);

Important Methods:
getName()
getValue()
setValue()
setMaxAge()
getMaxAge()

🔷 22. HttpSession

HttpSession session = request.getSession();
session.setAttribute("user", value);
session.getAttribute("user");
session.invalidate();

🔷 23. Web Server vs Application Server

Web Server:
• Handles HTTP
• Example: Tomcat

Application Server:
• Handles Web + Enterprise
• Example: WebLogic

====================================================

🔥 FINAL REVISION SUMMARY

✔ JDBC
✔ ResultSet
✔ RowSet
✔ CallableStatement
✔ Transactions
✔ ACID
✔ Metadata
✔ Servlet
✔ JSP
✔ MVC
✔ JavaBeans
✔ Session
✔ Cookies

📖 THE STORY OF JAVA COLLECTIONS

Aman Verma — Tue, 27 Jan 2026 11:46:56 +0000

Before JDK 1.2

Java developers mainly used:

Arrays (fixed size, rigid)
Legacy classes like Vector and Hashtable

Problems:

No standard structure
Inconsistent design
Hard to maintain and extend

JDK 1.2 — COLLECTIONS FRAMEWORK

Java introduced the Collections Framework to:

Standardize data storage
Provide reusable data structures
Improve consistency and flexibility

====================================================

🌱 ITERABLE (ROOT FOR TRAVERSAL)

Introduced: JDK 1.5
Package java.lang

Purpose:

Allows objects to be traversed

Supports:

for-each loop
Iterator

Note:

Iterable does NOT store data
It only allows traversal

====================================================

🧠 COLLECTION INTERFACE

Introduced: JDK 1.2
Package: java.util

Role:

Root interface of the Collections Framework
Defines basic behavior for all collections

Common Methods:

add()
remove()
contains()
size()
isEmpty()
clear()

Concept:
"If you store objects, you must support these operations."

====================================================

🌳 THREE CHILDREN OF COLLECTION

📋 LIST → ORDERED COLLECTION

Features:

Allows duplicate elements
Maintains insertion order
Index-based storage

Implementations:

ArrayList → Fast access
LinkedList → Fast insert/delete
Vector → Legacy (synchronized)

Use Case:

When order matters
When duplicates are allowed

🔒 SET → UNIQUE COLLECTION

Features:

Does NOT allow duplicates
No index-based access
Useshashing/sorting

Implementations:

HashSet → Fast, unordered
LinkedHashSet → Insertion order
TreeSet → Sorted order

Use Case:

When uniqueness is required

📥 QUEUE → FIFO COLLECTION

Introduced: JDK 1.5

Features:

Follows FIFO (First In First Out)
Used in scheduling and processing

Implementation:

PriorityQueue → Priority-based order

Use Case:

When processing order matters

====================================================

🧰 COLLECTIONS CLASS (UTILITY)

Package java.util

Features:

Utility class
Contains only static methods
Private constructor (cannot be instantiated)

Provides:

Sorting
Searching
Reversing
Synchronizing

Concept:
"Toolbox for working with collections"

====================================================

🔄 ITERATION MECHANISMS

🔄 ITERATOR

Introduced: J DK 1.2

Features:

Forward direction only
Works with all collections

Methods:

hasNext()
next()
remove()

Use Case:

Simple forward traversal

🔁 LISTITERATOR

Features:

Forward and backward traversal
Index-based navigation
Works only with List

Use Case:

Full control over List traversal

====================================================

⭐ FINAL INTERVIEW SUMMARY

Collection → Stores single objects
Map → Stores key–value pairs
List → Duplicates + Index
Set → No duplicates
Queue → FIFO
Iterator → Forward only
ListIterator → Forward & Backward
Vector,
Hashtable → Legacy

EVENT-DRIVEN ARCHITECTURE (EDA)

Aman Verma — Tue, 27 Jan 2026 02:24:38 +0000

▶ What is EDA?

A software architecture where systems communicate using events.
An event signals that something important happened (e.g., order placed, ride requested, video paused).
Services react to events instead of calling each other directly.

▶ Why EDA is Needed

Traditional request–response causes tight coupling.
As services grow, connections become complex and hard to scale.
Updating one service can break many others.
EDA decouples services, improving scalability and flexibility.

▶ Core Idea

Producers create events.
Consumers react to events.
Communication happens asynchronously via a messaging system.
Producers and consumers do NOT know about each other.

▶ Key Components

Event Producer: generates events.
Event Consumer: processes events.
Event Broker: routes events (queue/stream).
Event Contract: defines event structure and data.

▶ Real-World Examples

Streaming platforms track user actions as events (play, pause, rate) for recommendations and analytics.
Ride-sharing apps use events for matching drivers, pricing, ETA calculation, and traffic updates.
Multiple services consume the same event independently.

▶ EDA vs Service Mesh

EDA: asynchronous, event-based communication.
Service Mesh: synchronous service-to-service communication.
Both patterns often work together in microservices systems.

▶ Event Processing Types
1) Simple Event Processing

One event → one direct action.
Example: Order placed → update inventory.

2) Complex Event Processing

Multiple events analyzed together.
Example: Demand + traffic + drivers → surge pricing.

▶ Benefits of EDA

High scalability.
Independent service scaling.
Better fault tolerance.
Efficient real-time data processing.

▶ Challenges of EDA

Event ordering can be difficult.
Risk of duplicate event processing.
Eventual consistency instead of immediate consistency.
Requires careful design and monitoring.

▶ Common Tools

Distributed streaming platforms for high-scale events.
Cloud-based messaging services.
Lightweight brokers for smaller systems.

▶ Final Takeaway

EDA is not just a trend.
It powers modern systems that process billions of events daily.
Essential for scalable, real-time, microservices architectures.

JAVA REALITY !!

Aman Verma — Tue, 27 Jan 2026 02:15:25 +0000

▶ What Java Is ?

A verbose, boilerplate-heavy, object-oriented language.
Created during the dot-com bubble by Sun Microsystems.
Originally meant for TV remotes, released publicly in 1996.
Runs on billions of devices for decades.

▶ Java Philosophy

Motto: "Write once, run everywhere"
Reality: "Write once, debug everywhere"
Known for long logs, heavy configuration, and legacy systems.

▶ Ecosystem & Influence

Inspired JVM languages: Groovy, Clojure, Scala, Kotlin.
Also indirectly inspired JavaScript.
Failed attempt at the web: Java Applets (thankfully dead).

▶ Enterprise Reality

Often paired with Oracle Database.
Companies spend years talking about migrating away.
Expensive licenses, long-term enterprise lock-in.

▶ Getting Started Pain

Install JDK, JRE, JVM.
Prepare for massive error logs.
Memorize: public static void main(String[] args)

▶ Java Coding Style

Forced Object-Oriented Programming.
Requires classes even for Hello World.
Much more boilerplate than languages like Python.
Encourages deep inheritance and complex class hierarchies.

▶ Typical Java Project Lifecycle

Write one giant class.
Boss complains.
Split into deeply nested subclasses.
Code becomes impossible to refactor.
Developer questions life choices.

▶ Final Truth

Java is widely hated yet widely used.
Two types of languages: 1) Those people complain about 2) Those nobody uses
Love it or hate it, Java gets real work done.

COMPUTER SCIENCE 101 – SUMMARY

Aman Verma — Tue, 27 Jan 2026 02:09:36 +0000

▶ Coding Reality

Most developers debug by re-running code without understanding it.
You can earn well in software while treating computers like magic.

▶ What a Computer Is

A computer is essentially a Turing Machine.
Built from billions of transistors acting as ON/OFF switches.
Smallest unit: Bit (0 or 1)
8 bits = 1 Byte (256 values)
Characters mapped using ASCII / UTF-8.

▶ Binary & Machine Code

Computers use binary (base-2).
Humans prefer hexadecimal (base-16).
Code is converted into machine code for the CPU.

▶ Memory & Hardware

RAM stores data using memory addresses.
CPU + RAM = the brain of the computer.
Input devices (keyboard, mouse), Output devices (monitor).
OS (Linux, Windows, macOS) manages hardware via drivers.
Shell = interface to interact with OS (CLI, SSH).

▶ Programming Languages

Interpreted: Python (line-by-line execution).
Compiled: C/C++ (compiled to executable).
Abstraction hides hardware complexity.

▶ Variables & Memory

Variable names and data are stored in memory.
Dynamic typing (Python) vs Static typing (C).
Pointers store memory addresses.
The garbage collector manages memory automatically.

▶ Data Types

int, float, double for numbers.
char, string for text.
Big-endian vs Little-endian memory storage.

▶ Data Structures

Array/List: indexed, ordered.
Linked List: nodes with pointers.
Stack: LIFO
Queue: FIFO
Hash Map / Dictionary: key–value pairs.
Tree: hierarchical structure.
Graph: nodes + edges (relationships).

▶ Algorithms

Algorithms solve problems using data structures.
Functions take input → process → return output.
Expressions produce values; Statements perform actions.
Conditionals (if/else), Loops (for/while).
Recursion uses a call stack → risk of stack overflow.
Base condition prevents infinite recursion.

▶ Performance Analysis

Big-O notation measures efficiency.
Time complexity & Space complexity.

▶ Algorithm Types

Brute Force
Divide & Conquer (Binary Search)
Dynamic Programming (Memoization)
Greedy Algorithms (e.g., Dijkstra)
Backtracking (exploring all paths)

▶ Programming Paradigms

Declarative (what to do)
Imperative (how to do)
Functional, Procedural
Object-Oriented Programming (Classes & Objects)
Inheritance & Design Patterns

▶ Memory Areas

Stack: short-lived function calls.
Heap: long-lived objects, passed by reference.

▶ Concurrency & Parallelism

Threads enable parallel execution.
Concurrency via event loops & coroutines.

▶ Networking & Cloud

Virtual Machines simulate hardware.
IP addresses identify machines.
DNS maps URLs to IPs.
TCP handshake establishes a connection.
SSL encrypts data.
HTTP transfers web data.
APIs (REST) expose server data.

▶ Final Truth

You must learn printers.
Because grandma will ask you to fix them.

TECH TRENDS (2026)

Aman Verma — Sun, 25 Jan 2026 10:46:59 +0000

• Future feels overengineered, expensive, and unfinished

AI bosses, robot partners, smart devices with ads
Startup ideas = old ideas + “AI / Quantum”

• Software jobs

2023 hiring boom is gone, fewer openings
Still ~15% growth forecast till 2034
AI tools won’t replace engineers in 2026
New role emerging: "code janitors" fixing AI-written code
H1B visa changes make cheap foreign hiring harder

• AI bubble status

AI hype not over yet, but LLMs have plateaued
GPT-5 disappointed, no true intelligence leap
Many white-collar jobs at risk (managers, designers, analysts)
AI companies still private → IPO wave will signal peak
Possible major IPOs in 2026 (OpenAI, Anthropic, SpaceX)

• Robots & hardware

Humanoid robots (Neo, Tesla Optimus, Figure) are gaining hype
Tech is still weak, but funding is huge
2026 may see robots entering factories

• Wearables & XR

Past AI wearables failed (Rabbit, Humane Pin)
New attempts coming (OpenAI + Jony Ive)
VR/AR is still niche and mostly unprofitable
Apple Vision Pro flop, cheaper version rumored

• Chips & infrastructure

Biggest winners: Nvidia, ARM, TSMC
AI demand = massive GPU + electricity needs
Intel was saved by the US government, “too big to fail.”

• Energy & nuclear

Data centers running out of power
Nuclear energy revival possible
Small modular reactors for data centers are emerging

• Quantum computing

Breakthroughs in 2025–26
First real-world useful quantum algorithms
Could dwarf the AI boom
US vs China race intensifying

• Government tech

Digital IDs and CBDCs expanding in Europe & UK
Privacy concerns are increasing
Smartphones becoming state-controlled gateways

• JavaScript ecosystem

Node.js improves, native TypeScript support
Deno adds built-in bundler
Bun.js is gaining popularity (fast + built-ins)
React is still dominant but improving
New frameworks emerging (e.g., Ripple)

• Skill advice

Understanding AI fundamentals > hype
Strong engineering skills still matter
Learning how AI actually works is key

humorous history of programming

Aman Verma — Thu, 22 Jan 2026 08:22:06 +0000

==============================

HISTORY OF PROGRAMMING (SUMMARY)

BEGINNING

Initially, nothing existed

Number 1 invented → later 0 invented

People thought numbers were useless

ELECTRICITY & BINARY

Electricity works as ON / OFF

ON = 1

OFF = 0

Combination of 1 and 0 → Binary system

Computers understand voltage, not words

BITS & BYTES

Single binary digit = Bit

8 bits grouped = Byte

1 Byte can represent numbers from 0 to 255

EARLY COMPUTING

Used vacuum tubes and punch cards

Programming done using pure 0s and 1s

Very difficult and error-prone

ASSEMBLY LANGUAGE

Created to replace raw binary

Uses human-readable mnemonics

Still hardware dependent

Easier than machine code but still complex

COMPILERS & HIGH-LEVEL LANGUAGES

Compiler converts readable code → machine code

A programmer never sees machine code again

First high-level languages:

FORTRAN → scientific computing

COBOL → business & government systems

LISP & NEW IDEAS

Everything is a list

Code = Data, Data = Code

Uses an interpreter instead of a compiler

Introduced Garbage Collection

Memory handled automatically

STRUCTURED PROGRAMMING

GOTO statements discouraged

Focus on readable & maintainable code

C LANGUAGE

Fast and powerful

Direct access to memory

High performance but unsafe

Used to build operating systems

UNIX

Built using C

Philosophy:

Small programs

Each program does one task well

Programs work together using pipes

Command-line culture begins

C++

Extension of C

Added Object-Oriented Programming

Concepts:

Classes

Objects

Inheritance

Polymorphism

Widely used in games, browsers, and engines

1980s LANGUAGE BOOM

BASIC → beginner friendly

Pascal → structured programming

Ada → military

Erlang → telecom systems

Smalltalk → pure object-oriented

Many languages copied Smalltalk concepts

1990s REVOLUTION

Python → readable, indent

DATA STRUCTURE

Aman Verma — Tue, 13 Jan 2026 06:02:30 +0000

A. Introduction

Course Overview
How to Navigate the Course
Choosing Programming Language
Problem-Solving Approach

B. Warm Up / Programming Basics

Programming Fundamentals
Functions
Conditional Statements (if-else)
Loops (for, while, do-while)
Nested Loops
Star Pattern Problems
Second Largest Element
Count Digits
Palindrome (Number/String)
Reverse Integer

C. Time & Space Complexity

Time Complexity
Space Complexity
Big-O Notation
Best, Worst & Average Case

D. Arrays (Easy / Medium)

Array Basics
Remove Duplicates
Remove Element
Reverse String
Best Time to Buy & Sell Stock
Merge Sorted Arrays
Move Zeros
Max Consecutive Ones
Missing Number
Single Number

E. Recursion (Easy / Medium)

Recursion Basics
Base Case & Recursive Case
Sum of First N Numbers
Sum of Array Elements
Factorial
Power of Two
Recursion Patterns

f. Searching & Sorting

Linear Search
Binary Search
Bubble Sort
Selection Sort
Insertion Sort
Merge Sort

G. Linked List (Easy / Medium)

Introduction to Linked List
Design Linked List
Insert Nodes
Delete Nodes
Middle of Linked List
Reverse Linked List
Cycle Detection (Hashing & Floyd’s Algorithm)
Palindrome Linked List
Intersection of Linked Lists
Remove Nth Node (One-Pass & Two-Pass)
Remove Duplicates
Odd-Even Linked List
Add Two Numbers
Merge Two Sorted Lists
Rotate Linked List
Swap Nodes (Iterative & Recursive)

H. Strings (Easy / Medium)

Length of Last Word
Find Words Containing Character
Jewels and Stones
Frequency of Vowels & Consonants
Balanced Strings
Reverse String II
Valid Palindrome
Largest Odd Number in String
Longest Common Prefix
Valid Anagram
Isomorphic Strings
Group Anagrams

I. Stack & Queue

Stack & Queue Basics
Stack Operations
Queue Operations
Stack using Queues
Queue using Stacks
Valid Parentheses
Min Stack
Remove Outermost Parentheses
Reverse Polish Notation
Next Greater Element (I & II)
Daily Temperatures
Rotting Oranges

J. Binary Search Algorithm

Square Root of X
Safe Middle Element Calculation
Guess Higher or Lower
Search in Rotated Sorted Array
First Bad Version
Find Peak Element
Minimum in Rotated Sorted Array
First & Last Position in Sorted Array
Peak in Mountain Array
Single Element in Sorted Array
K Closest Elements

K. Two Pointers & Sliding Window

Two Sum
Two Sum II
Is Subsequence
First Occurrence in String
KMP Algorithm
Intersection of Linked Lists
Container With Most Water
Three Sum
Trapping Rain Water
Longest Substring Without Repeating Characters
Longest Repeating Character Replacement
Permutation in String
Sliding Window Maximum

L. Binary Tree

Tree Basics
Tree Traversals
DFS & BFS
Level Order Traversal
Maximum Depth
Path Sum
Symmetric Tree
Invert Tree
Same Tree
Balanced Binary Tree
Diameter of Binary Tree
Zigzag Traversal
Subtree Checking
Lowest Common Ancestor
Right Side View
Count Good Nodes
Populate Next Right Pointer
Maximum Path Sum

M. Binary Search Tree (BST)

BST Introduction
Valid BST
Search in BST
Insert into BST
Kth Smallest Element
Lowest Common Ancestor in BST

N. Heap / Priority Queue

Heap Basics
Build Heap
Insert & Delete
Heapify
Heap Sort
Priority Queue
Kth Largest Element
Kth Largest in Stream
Last Stone Weight
Top K Frequent Elements
Kth Smallest in Sorted Matrix

O. Hashing

Hash Table Basics
Cycle Detection
Palindrome Linked List
Linked List Intersection
Remove Duplicates
Character Frequency
Valid Anagram
Isomorphic Strings
Group Anagrams
Next Greater Element
Two Sum II
KMP Algorithm
Permutation in String
Sliding Window Maximum

P. Backtracking

Backtracking Basics
Subsets / Power Set
Combinations
Permutations
Subsets II
Combination Sum I, II, III
Letter Combinations
Palindrome Partitioning
Word Search
N-Queens

Q. Greedy Algorithm

Greedy Strategy
Two City Scheduling
Assign Cookies
Lemonade Change
Stock Buy & Sell II
Insert Interval
Merge Intervals
Partition Labels
Non-Overlapping Intervals
Task Scheduler
Gas Station
Car Pooling
Candy Distribution

R. Dynamic Programming

DP Fundamentals
Top-Down & Bottom-Up
Fibonacci DP
Climbing Stairs
Min Cost Climbing Stairs
House Robber I & II
Coin Change I & II
Palindromic Substrings
Longest Palindromic Substring
Decode Ways
Kadane’s Algorithm
Max Product Subarray
Word Break
LIS
Partition Equal Subset Sum
Unique Paths

19. Graphs

Graph Basics
BFS & DFS
Path Existence
Cycle Detection
Topological Sort
Shortest Path Algorithms
Dijkstra
Bellman-Ford
Floyd-Warshall
Minimum Spanning Tree
Prim’s Algorithm
Kruskal’s Algorithm
Union Find
Network & Flight Problems

S. Tries

Trie Introduction
TrieNode Structure
Insert, Search, Prefix
Time & Space Complexity