Forem: Ankush Singh Gandhi

WarriorOS: Building a Modern Terminal Portfolio with React, Gemini, and Cloud Run

Ankush Singh Gandhi — Sun, 01 Feb 2026 17:04:52 +0000

This is a submission for the New Year, New You Portfolio Challenge Presented by Google AI

About Me

Hi everyone! I'm Ankush Singh Gandhi, a software engineer and the "Warrior Who Codes." I specialize in full-stack development, system design, and building interactive web experiences.

But I'm not just a coder—I'm a passionate technical writer and lifelong learner. Over the last few years, my articles on Dev.to have gathered over 48,000 views, and I was previously recognized as a Top Voice in Computer Science on LinkedIn for my contributions to community discussions. I absolutely love devouring system design blogs and engineering case studies.

My goal with this portfolio was to create something that reflects this duality: a place that showcases my projects but also serves as an "Interactive Hub" for the content I create and consume. It blends a retro-terminal aesthetic with modern, high-performance UI to tell my story.

The Evolution: WarriorOS (v4.0) vs. Original

⚠️ NOTE: Work in Progress
This project is currently in active development. You are seeing a live preview of the new WarriorOS iteration.

This project is a complete reimplementation of my original portfolio, warriorwhocodes.com. While the original site served me well, I wanted to push the boundaries of what a personal site could be—moving from a static showcase to a living, breathing operating system.

� CRITICAL VIEWING EXPERIENCE RECOMMENDATION
While I have embedded the site below, I strongly prefer and recommend opening the portfolio in a New Window.
The complex terminal shortcuts, hover effects, and immersive UI are best experienced directly, not inside an iframe.

👉 Click Here to Open WarriorOS in Full Screen >>

1. The New Architecture (WarriorOS)

Status: Active Development 🚧
Focus: Terminal-first navigation, AI integration, "One-Shot" learning modules.

2. The Original Portfolio (Legacy)

Status: Stable (v3.0)
Focus: Standard visual showcase.

Warrior Who Codes - Ankush Singh Gandhi

Backend & Software Developer specializing in Python, Django, Flask, and cloud systems. Also building modern web apps and Flutter-based mobile apps.

warriorwhocodes.com

How I Built It

I built WarriorOS (my portfolio) using a modern tech stack centered purely on offering a seamless user experience, accelerated by Google's AI tools.

Tech Stack

Frontend: React (Vite), Tailwind CSS, Framer Motion for animations.
Deployment: Google Cloud Run (Dockerized Nginx container).
AI Integration: Google Gemini API.
Development Partner: Built using Antigravity, Google's advanced agentic coding assistant.

Building with Antigravity & Gemini

This project didn't just use AI features; it was built with AI. I used Antigravity (powered by Gemini) to pair-program the entire "WarriorOS".

Rapid Prototyping: I used the agent to iterate on the complex "Terminal Button" UI until it matched my exact vision—down to the pixel-perfect glow effects.
Deployment Automation: The agent handled the containerization with Docker and the Cloud Run deployment commands, saving me hours of configuration time.

Google Gemini API Integration (The "New You" Feature)

I integrated the Gemini API directly into the site to create an AI Expert & Companion that doesn't just chat, it adapts. But more than just a chatbot, this feature represents my philosophy on Modern Content Writing: The AI + Human Symbiosis.

The Mindset: Evolution of Technical Learning

In the past, technical writing was static. You wrote a blog, someone read it, and if they got stuck, they had to leave your site to Google the answer.

I believe the future of content is interactive and adaptive.

The Human Role: My job is to curate the structure, the roadmap, and the core insights (the human experience).
The AI Role: Gemini's job is to be the personalized expert that adapts this content specifically for you.

With the new Personalization Profile engine, the AI doesn't just "answer questions" — it knows who you are. An Aspiring Backend Engineer learning Redis needs different analogies than a Senior Frontend Dev or a Product Manager. By letting you configure your Occupation, Goal, and Skills, WarriorOS transforms static documentation into a bespoke mentorship session.

Where is Gemini Integrated?

Context-Aware AI Personalization:
- Open the AI Toolbox via the floating button.
- Click [CONFIGURE AI] to set your profile (e.g., Occupation: "Student", Goal: "System Design").
- The Magic: Now, every AI tool—from the Pathfinder to the Tutor—will reshape its responses to fit your background. The Tutor uses your specific skills in examples, and the Quiz adjusts its difficulty automatically.
The "One-Shot" Expert:
- Navigate to the Library and open any "One-Shot" roadmap.
- The AI Expert uses Gemini 2.5 Flash to provide responses that are not just accurate but streamed in real-time for a natural conversational feel. It holds the full context of the roadmap you're reading, letting you ask deeper questions without losing scope.
Global AI Assistant:
- Throughout the site, the AI acts as a guide to my portfolio. It can explain the architecture of my projects, suggest which blog post you should read based on your interests, or even quiz you on the skills listed in my "About Me" section.
Active Recall Quizzes:
- Inside the Expert window, you can switch to "Quiz Mode." Gemini generates rapid-fire questions based on the content you're viewing, helping you verify your understanding in real-time.

What I'm Most Proud Of

The Global Terminal System: I didn't want the terminal to be a fake GIF. It's a fully functional global navigation CLI. No matter where you are on the site, you can hit the button and type commands like:
- system.archive_access -> Instantly navigates to my library/blog section.
- system.profile_bio -> Jumps to the About Me section.
- python -> Launches a simulated Python environment (try import antigravity!). It treats the entire website navigation like a file system, giving power users a faster way to explore.

Semantic Terminal Navigation:
This is where the magic happens. I integrated Google Gemini's capabilities directly into the CLI. You aren't limited to memorizing strict commands.
* Ask naturally: Show me what you did at Desi Diaries
* Express intent: I want to see your backend projects
* Get context: Who are you?

    The terminal intelligently parses your intent. If you want to navigate, it executes the system command. If you have a question about my work experience, it references my portfolio data to give you a concise, terminal-style answer. It transforms the CLI from a tool for power users into a conversational interface for everyone.

The "Configure AI" Ecosystem:
I built a dedicated Settings Configuration UI where users can input their occupation and goals (e.g., "Student learning DSA"). This isn't just a form—it's the brain of the site. It stores your profile locally and dynamically injects it into every single AI interaction, making the "AI Expert" feel like a persistent companion rather than a stateless bot.
The Personalized Mentorship Hub:
Reflecting my passion for technical writing, I built interactive roadmap pages for topics like Redis, Django ORM, and MySQL. But instead of just static guides, I've created a hub where the AI Expert acts as a personal mentor, analyzing my written content through the lens of your career goals and occupation.
Seamless Cloud Deployment:
Getting the app Dockerized and running on Google Cloud Run was a huge win. The site loads instantly, is secured with HTTPS by default, and can scale up if my blog post goes viral.

I'm excited to share WarriorOS with the community. It represents my journey from writing articles to building an entire operating system for my work.

Source Code

You can explore the full source code for WarriorOS on GitHub:

AnkushSinghGandhi / portfolio-website-v5

WARRIOR OS (v4.0)

A Modern Terminal-Inspired Portfolio & Engineering Archive

Live Preview >>

🚀 Overview

WarriorOS is a complete reimplementation of the warriorwhocodes.com portfolio. It moves away from static web design towards an "Interactive Operating System" aesthetic, blending a retro-terminal CLI with modern, high-performance UI components.

Built as a submission for the New Year, New You Portfolio Challenge, this iteration focuses on human-AI symbiosis in technical education.

✨ Key Features

🌐 Global Terminal (Warrior_OS CLI): A fully functional command-line interface used for navigation and system interaction.
🤖 AI Expert & Companion: Integrated Google Gemini API that acts as a context-aware tutor for roadmaps and technical content.
🗺️ One-Shot Roadmaps: Interactive, deep-dive learning modules on topics like Redis, MySQL, and Django ORM.
⚡ High-Contrast Aesthetics: A "Paper Terminal" theme featuring sharp black cards over white grid backgrounds for maximum readability.

🛠️ Tech Stack

Frontend: React…

View on GitHub

Happy Coding! 🚀

Tips, Strategies, and Patterns to Solve and Identify DSA Problems

Ankush Singh Gandhi — Fri, 03 Oct 2025 06:41:15 +0000

One of the biggest struggles in mastering Data Structures and Algorithms (DSA) is not writing code — it’s recognizing patterns in problems. Once you can spot the underlying pattern, solving the problem becomes much easier.

This guide is a cheat sheet + strategy blog to help you identify DSA problem types and choose the right approach.

🔑 General Rules to Spot Patterns

1. If the input array is sorted →

Try Binary Search
Or Two Pointers

2. If asked for all permutations/subsets →

Use Backtracking

3. If the problem involves a Tree →

Use DFS or BFS

4. If the problem involves a Graph →

Again, DFS or BFS (with variations like Union-Find, Topological Sort, etc.)

5. If the problem involves Linked Lists →

Think of Two Pointers (slow/fast pointer tricks)

6. If recursion is not allowed →

Use a Stack to simulate recursion

7. If in-place solution is required →

Use swapping, or
Store extra info inside existing pointers

8. If asked for maximum/minimum subarray/subset/option →

Use Dynamic Programming (Kadane’s, Knapsack, etc.)

9. If asked for top/least K items →

Use Heap or QuickSelect

10. If asked for common strings/words →

* Use **HashMap** or **Trie**

11. Else:

* Use **Map/Set** for O(1) lookup with O(n) space
* Or **Sort** input for O(n log n) time and O(1) space

🧠 The Two-Pointer Pattern

🔍 How to Identify

Finding pairs, triplets, or subarrays.
Problem mentions sorted array.
Problem wants in-place solution (O(1) space).
Need O(n) or O(n log n), brute force O(n²) is too slow.

🚀 Approaches

Opposite Ends Approach

i = 0, j = n-1
Move inward based on condition 👉 Examples: Two Sum (sorted), Container With Most Water, Valid Palindrome

Same Direction Approach (Slow/Fast Pointers)

Both start at 0, fast pointer explores, slow pointer lags behind 👉 Examples: Remove Duplicates, Linked List Cycle, Sliding Window variations

💡 Tip: When modifying in-place, slow pointer usually marks “where to write”.

📝 Pointer Movement Table

Condition	Action
Sum too small	Move left++
Sum too big	Move right--
Found valid pair	Move both inward
Skip duplicates	Move pointer at dup

⚡ The Sliding Window Pattern

🔍 How to Identify

Problems asking for subarray/substring with constraints:
- Maximum/minimum sum/length/count
- K distinct elements
- Character replacements

🚀 Strategies

Expanding Window (for max problems):
Grow window until condition is invalid, then shrink.
Shrinking Window (for min problems):
Shrink while condition holds, track min length.

📝 Quick Rules

Use if inside for loop when at most one adjustment is needed per step (soft constraint).
Use while loop when multiple shrinks may be required (strict constraint).

👉 Examples: Longest Substring Without Repeating Characters, Minimum Window Substring

🌳 Tree & Graph Patterns

DFS → when recursion or full exploration is needed.
BFS → when solving shortest path (unweighted) or level-order.
Union-Find → when dealing with connectivity/cycles.
Topological Sort → for dependency/ordering problems.

👉 Examples:

Tree Traversals (Inorder/Preorder/Postorder)
Shortest Path in Graph (BFS, Dijkstra)
Detecting Cycles (DFS, Union-Find)

🧮 Dynamic Programming (DP)

🔍 How to Identify

Problem asks for minimum/maximum something.
Overlapping subproblems appear.
Brute force recursion seems exponential.

🚀 Common DP Problems

Kadane’s Algorithm (max subarray sum)
Knapsack variations
Fibonacci/Climbing Stairs
Longest Common Subsequence / Substring

👉 Tip: Always try recursion first → then memoize → then bottom-up.

🏗️ Heaps and Priority Queues

Use when asked for top K elements, running median, or minimum/maximum stream values.
Heap provides O(log n) insertion and O(1) top element.

👉 Examples: Kth Largest Element, Merge K Sorted Lists, Sliding Window Maximum

🔡 Strings & Hashing

HashMap / Set → when you need to check existence/count (anagrams, duplicates).
Trie → when asked for prefix/suffix queries, autocomplete, or dictionary problems.

👉 Examples: Group Anagrams, Word Search II, Longest Prefix

🧭 Checklist for Any DSA Question

When you see a new problem, ask:

Is the input sorted? → Try binary search / two pointers.
Is it about subarray/substring? → Try sliding window.
Is it about permutations/subsets? → Try backtracking.
Is it about a tree/graph? → Try DFS/BFS.
Is it about max/min result? → Try DP.
Is it about Kth element/top items? → Try heap/QuickSelect.
Is it about strings/prefixes? → Try HashMap/Trie.
Else → Try Map/Set or Sorting.

🎯 Final Tip

👉 Brute force first, optimize later.
Most optimizations are about recognizing the pattern and then replacing nested loops or recursion with the right technique.

By building this pattern recognition skill, you’ll start to “see through” DSA problems instead of brute-forcing every new one. That’s the mindset of a strong problem solver.

🌐 Essential API & HTTP Interview Questions

Ankush Singh Gandhi — Thu, 02 Oct 2025 16:16:25 +0000

APIs and HTTP form the backbone of modern web communication. Below are the most important API & HTTP interview questions every developer should know.

🔹 What is an API and why do we use it?

API (Application Programming Interface) → A set of rules that allow software applications to communicate with each other.
Use: Enables integration, automation, and reusability (e.g., payment gateway, social login, weather services).

🔹 What are HTTP Methods?

GET → Retrieve data.
POST → Create new resources.
PUT → Update/replace entire resource.
PATCH → Partially update resource.
DELETE → Remove resource.
HEAD → Retrieve headers only.
OPTIONS → Discover supported methods.

🔹 GET vs POST

GET → Idempotent, data in URL, not secure for sensitive info, limited length.
POST → Non-idempotent, data in body, secure for large/sensitive payloads.

🔹 PUT vs PATCH

PUT → Replaces entire resource.
PATCH → Updates specific fields in resource.

🔹 PUT and POST

PUT → Used for updating/replacing. Idempotent.
POST → Used for creating. Non-idempotent.

🔹 SOAP API and Its Use

SOAP (Simple Object Access Protocol) → XML-based protocol for exchanging structured information.
Use: High security, reliable messaging, enterprise systems like banking/telecom.

🔹 REST API

REST (Representational State Transfer) → Architectural style using HTTP for CRUD operations on resources.
Lightweight, scalable, widely used in web/mobile apps.

🔹 SOAP vs REST

SOAP → Strict, XML only, heavier, built-in security.
REST → Flexible, supports XML/JSON, faster, easier to use.

🔹 HTTP Status Codes

1xx → Informational.
2xx → Success (200 OK, 201 Created).
3xx → Redirection (301 Moved Permanently).
4xx → Client Error (400 Bad Request, 404 Not Found).
5xx → Server Error (500 Internal Server Error).

🔹 Components of HTTP Request & Response

Request → Method, URI, Headers, Body (optional).
Response → Status line, Headers, Body (optional).

🔹 GET/POST Instead of PUT for Resource Creation

While POST is standard for creation, some APIs use PUT if the client knows the resource URI.
Using GET for creation is a bad practice (GET should be read-only).

🔹 RESTful Web Service & Features

RESTful WS → Service following REST principles.
Features:
- Client-server architecture
- Stateless communication
- Uniform interface
- Cacheable responses
- Layered system

🔹 Disadvantages of RESTful Web Services

No strict standards (loose rules).
Less secure compared to SOAP.
Overhead of multiple requests for complex operations.

🔹 What’s Payload?

Payload = Actual data sent in request/response body (e.g., JSON/XML).

🔹 Can We Send Payload in GET or DELETE?

Technically possible but not recommended.
HTTP spec doesn’t forbid it, but many servers/proxies ignore it.

🔹 Max Payload Size in POST

No official limit in HTTP spec.
Practical limit depends on server (e.g., Apache, Nginx default ~2MB to 100MB).

🔹 Markup Language for REST API

REST supports multiple formats, most commonly JSON and XML.

🔹 XML vs JSON

XML → Verbose, supports attributes, widely used in SOAP.
JSON → Lightweight, human-readable, faster, widely used in REST.

🔹 What’s Resource in REST & How to Present?

Resource = Any entity (user, product, order).
Represented by URI (Uniform Resource Identifier). Example: /api/users/123

🔹 Advantages of REST in Web API

Lightweight and scalable.
Platform/language independent.
Uses existing HTTP methods.
Easy to integrate with web/mobile.

🔹 Concept of Statelessness in REST API

Each request contains all info (no server-side session).
Improves scalability, reduces server memory usage.

🔹 What’s URI?

URI (Uniform Resource Identifier) → Unique address for a resource. Example: https://api.example.com/products/1

🔹 CORS in Web API

CORS (Cross-Origin Resource Sharing) → Security feature of browsers.
Allows or restricts requests from different domains.

🔹 What’s Postman & Its Use?

Postman → API client tool for testing, documenting, and automating API calls.
Widely used for debugging and collaboration in teams.

✅ Conclusion

APIs and HTTP form the foundation of modern web services. Knowing HTTP methods, REST principles, SOAP vs REST, status codes, payloads, and tools like Postman is critical for acing interviews and real-world backend development.

🚀 Essential Git and GitHub Interview Questions

Ankush Singh Gandhi — Thu, 02 Oct 2025 16:09:20 +0000

Git and GitHub are fundamental tools for developers, and interviewers often test your knowledge of them. This blog covers the most commonly asked Git and GitHub interview questions, explained in a crisp and practical way.

🔹 Git vs GitHub

Git: A distributed version control system (VCS) used to track source code changes.
GitHub: A cloud-based hosting platform for Git repositories with collaboration features like pull requests, issues, and CI/CD.

🔹 Repo vs Branch

Repository (Repo): A collection of files and their entire version history.
Branch: A lightweight movable pointer to a commit, allowing parallel development.

🔹 Push vs Pull

Push: Uploads your local commits to a remote repository.
Pull: Fetches updates from the remote and merges them into your local branch.

🔹 Pull vs Fetch

Pull = Fetch + Merge.
Fetch: Downloads changes from remote but does not merge automatically.

🔹 Clone vs Remote

Clone: Creates a local copy of a remote repo.
Remote: A reference (like origin) to the remote repository URL.

🔹 Diff vs Status

git diff: Shows actual changes (line-by-line) not staged or committed.
git status: Shows the state of working directory and staging area.

🔹 Merge vs Rebase

Merge: Combines histories of two branches with a new merge commit.
Rebase: Reapplies commits of one branch onto another, resulting in a linear history.

🔹 What’s a Git repo & how to create one?

A Git repository is a directory where Git tracks files and history.

git init          # Create a local repo  
git clone <url>   # Clone a remote repo

🔹 Fix a Broken Commit

git commit --amend        # Fix last commit  
git reset --soft HEAD~1   # Undo last commit, keep changes

🔹 What happens if `.git` folder gets deleted?

The repo loses its history and version control, as .git contains all commit objects, refs, and configurations.

🔹 Use of Staging Area (Indexing)

Temporary area where changes are reviewed before committing.
Allows selective commit (git add <file>).

🔹 Git Repo Hosting Functions

Platforms like GitHub, GitLab, Bitbucket provide:

Remote hosting
Collaboration (pull requests, issues)
CI/CD pipelines
Access control

🔹 VCS (Version Control System)

Tracks changes in code over time, supports collaboration, and helps rollback or branch development.

🔹 Features/Advantages of Git

Distributed VCS (no single point of failure).
Lightweight branching and merging.
Offline work support.

🔹 Git vs SVN

Git: Distributed, faster, branching-friendly.
SVN: Centralized, slower for branching, single point of failure.

🔹 `git config`

Sets user info and preferences.

git config --global user.name "Ankush"  
git config --global user.email "ankush@example.com"

🔹 `git ls-tree`

Shows content of a tree object (commits, blobs, trees).

🔹 Git Stash Commands

Save work: git stash
List stashes: git stash list
Apply latest: git stash apply
Drop stash: git stash drop

🔹 Revert vs Reset

Revert: Creates a new commit that undoes changes. Safe for shared repos.
Reset: Moves HEAD to a previous commit, can discard changes.

🔹 HEAD in Git

HEAD is the current branch pointer (latest commit).
Normally 1 active HEAD, but multiple branch heads can exist.

🔹 Git Username and Email

Each commit uses configured username & email (git config).

🔹 Commit History

git log  
git log --oneline --graph

🔹 Branch Operations

New branch: git branch new-feature
Switch branch: git checkout main OR git switch main
List branches: git branch
Create + switch: git checkout -b feature
Delete branch: git branch -d feature
Merge branch: git merge feature

🔹 Remote Tracking

Check which branch is being tracked:

git branch -vv

🔹 Force Push

git push origin main --force

🔹 Add Remote Repo

git remote add origin <url>

🔹 Rename File in Git

git mv old.txt new.txt  
git commit -m "Renamed file"

🔹 `.gitignore`

Defines files/folders Git should ignore. Example:

node_modules/  
*.log  
.env

🔹 Rollback Last Commit

git reset --soft HEAD~1     # Keep changes  
git reset --hard HEAD~1     # Discard changes

✅ Final Thoughts

These questions cover core Git and GitHub concepts often asked in interviews. Knowing both commands and theory will help you stand out in backend or full-stack interviews.

Build an ATS-Friendly Resume That Gets Seen (with Free LaTeX Template 🚀)

Ankush Singh Gandhi — Wed, 03 Sep 2025 14:42:52 +0000

Most resumes fail before a human even reads them.

Why? Because Applicant Tracking Systems (ATS) – the software recruiters use to filter applications – can’t parse overly designed templates. So no matter how pretty your resume looks in Canva, if the ATS can’t read it, your application goes straight to the rejection pile.

That’s why I built and shared a plain, single-column, ATS-friendly LaTeX resume template → ASG ATS Resume Template

In this blog, I’ll cover:

✅ Why plain templates are essential
✅ Actionable resume writing tips
✅ How to structure your resume effectively
✅ The LaTeX template I created (and why it’s optimized for ATS + recruiters)
✅ Step-by-step guide: How to use the template from GitHub in Overleaf

Why Plain Templates Work Best

Recruiters spend 6–8 seconds skimming your resume.
Fancy graphics, two-column layouts, and headshots only make it harder for both ATS software and humans to parse your resume.

The reality:

ATS reads resumes line by line, left to right → two columns confuse parsing.
Logos, icons, and tables often get skipped or jumbled.
Even recruiters don’t have time for visual clutter – they just want keywords, skills, and impact.

A great resume should be:

Plain and single-column → ATS-friendly
Keyword-rich → Matches job description
Skimmable → Recruiters can pick impact in seconds
One-page → if you have ≤5 years of experience

Resume Writing Tips 🔑

Here are the tips I’ve followed and baked into the template:

Structure

✅ Start with Work Experience (or Projects if fresher)
✅ Add a Summary section with job-relevant keywords
✅ Use bullet points for all achievements
✅ Place Skills section at the end – prove them earlier in projects/experience

Bullet Points

✅ Keep them one line (70–180 chars)
✅ Don’t end with a period
✅ Show impact with numbers (“Reduced API latency by 40%”)
✅ Don’t overuse action verbs – mix clarity + metrics

General Rules

✅ No photo on your resume – ATS doesn’t process images
✅ Skip 10th/12th school details if you have higher ed or experience
✅ Deployed projects? Add a hyperlink
✅ Only add relevant achievements & hobbies
✅ Use bold smartly – to catch the eye in 2 seconds

Why I Built the ASG ATS Resume Template 📝

When I started experimenting with resumes, I realized most templates online were either:

Too flashy → not ATS-friendly
Too plain → didn’t highlight keywords or impact

So I built my own LaTeX template:

Features:

📄 Single-column design for ATS parsing
✒️ Uses Tinos font (ATS-safe + clean for recruiters)
─── Thin dividers (no heavy graphics)
🔑 Bold project titles, plain descriptions underneath
📌 Bullet points optimized for readability
📍 Works 100% on Overleaf, GitHub, or local LaTeX install

👉 Try it here: ASG ATS Resume Template

Technical Deep-Dive: How the Template Works ⚙️

If you’re a developer (and especially if you’ve never touched LaTeX before), here’s what you’ll appreciate:

Minimal packages → No bloat, just geometry, enumitem, hyperref, and fontspec.
Exact alignment → Uses tabular* for job/project alignment (role left, date right).
Custom Section Command → A \Section{} command renders uppercase titles + thin horizontal rule.
Consistent spacing → parskip + tuned margins for a 1-page layout.
Font control → \setmainfont{Tinos} ensures ATS-readable + modern look.

Here’s a small snippet that makes section headers neat:

% Section command: UPPERCASE bold heading + thin rule
\newcommand{\Section}[1]{%
  \vspace{6pt}\noindent{\large\bfseries\MakeUppercase{#1}}\\
  \noindent\rule{\textwidth}{0.4pt}\vspace{6pt}}

And here’s how jobs/projects are aligned perfectly:

% Job line helper
\newcommand{\JobLine}[2]{%
  \noindent\begin{tabular*}{\textwidth}{@{}l@{\extracolsep{\fill}}r@{}}%
  #1 & #2\\\end{tabular*}}

That’s the power of LaTeX → precision control while keeping the output clean and consistent.

🚀 How to Use the Template from GitHub on Overleaf

This template works out of the box on Overleaf, so you don’t need to install LaTeX locally.

Here’s the step-by-step:

Go to the GitHub Repo → ASG ATS Resume Template
Download as ZIP → Click the green “Code” button → Download ZIP.
Open Overleaf → Log into overleaf.com.
Upload Project → New Project → Upload ZIP.
Switch Compiler → In Overleaf menu → Menu > Compiler > XeLaTeX.

⚠️ Important: The template uses fontspec, which only works with XeLaTeX/LuaLaTeX (not pdfLaTeX).
1. Edit main.tex → Replace name, work experience, projects, etc.
2. Recompile → Click “Recompile” and download your PDF.

✅ That’s it – your ATS-optimized resume is ready.

Who Should Use This Template?

🎓 Students/freshers → Swap “Experience” with “Projects”
👨‍💻 Software developers → ATS loves tech keywords, this layout handles them well
📈 Early-career professionals (1–5 years) → Perfect for a one-pager resume
🚀 Anyone applying online → ATS parsing guaranteed

Final Thoughts

Your resume isn’t about looking pretty – it’s about getting seen and understood quickly.
With a plain, ATS-friendly template, you instantly improve your odds of making it past the automated filters and into the hands of a recruiter.

That’s why I built the ASG ATS Resume Template → Grab it here

💬 I’d love to hear from you:
👉 What’s the biggest mistake you’ve seen people make on resumes?

✍️ PS: This template is free to use and customize. If it helps you land an interview, let me know!

💡 TIME COMPLEXITY PRIMER – Understand Big O Like a Kid With Candies 🍬

Ankush Singh Gandhi — Sun, 29 Jun 2025 18:38:29 +0000

🧠 What is Time Complexity?

Think of time complexity like asking:

"How many steps will my program take as the input gets bigger?"

Imagine you're:

🧸 Putting LEGO blocks one by one (O(n))
🎲 Checking only the first one (O(1))
📚 Flipping every page of a big book to find a word (O(n))
🕵️ Searching in a sorted drawer by cutting it in half every time (O(log n))

🍭 Big O Notation – Like Candy Boxes!

Let’s say you have n candies:

✅ O(1) — "I take 1 candy, no matter how many I have"

def get_first(candies):
    return candies[0]

No matter if you have 10 or 10,000 candies — you just grab the first one. 🎯

✅ O(n) — "I check every candy"

def find_sour_candy(candies):
    for candy in candies:
        if candy == 'sour':
            return True

If there are 5 candies, you may look 5 times. If there are 100? You might look 100 times!

✅ O(n²) — "I compare every candy with every other candy"

def find_duplicates(candies):
    for i in candies:
        for j in candies:
            if i == j and i != j:
                return True

Imagine you’re checking every candy against every other candy — it gets super slow when the pile grows! 🍬🍬🍬

✅ O(log n) — "I cut the candy box in half each time!"

def binary_search(candies, target):
    left, right = 0, len(candies) - 1
    while left <= right:
        mid = (left + right) // 2
        if candies[mid] == target:
            return True
        elif candies[mid] < target:
            left = mid + 1
        else:
            right = mid - 1

Smart search! Cut your pile in half every time until you find the candy 🍭

✅ O(n log n) — "Sort candies smartly!"

Merge sort, quicksort — faster than checking every pair like O(n²), but slower than O(n)

🍕 Analogy Recap:

Big O	Like this...
O(1)	Grab the first candy 🍬
O(n)	Taste every candy one by one 😋
O(n²)	Compare every candy with every other candy 😵
O(log n)	Smart guess by cutting box in half each time 🔪
O(n log n)	Smart sorting like organizing Lego blocks fast 🧱

🧩 Exercise 1: Candy Basket 🍬

You have a basket of n candies. You want to find if there's any red candy.

def has_red(candies):
    for candy in candies:
        if candy == "red":
            return True

❓ What's the time complexity?

✅ Answer: O(n) — you may need to check all the candies!

🧩 Exercise 2: Toy Shelf 🧸

You have a list of 10 toys. You always play with the first one.

def play_with_toy(toys):
    return toys[0]

❓ Time complexity?

✅ Answer: O(1) — always 1 step, no matter how many toys!

🧩 Exercise 3: Checking Every Friend's Name 👧👦

You want to say hi to every friend at the party.

def say_hi(friends):
    for friend in friends:
        print("Hi", friend)

❓ Time complexity?

✅ Answer: O(n) — say "Hi" once per friend!

🧩 Exercise 4: Double Trouble 🎭

You want to check every pair of kids to see if they’re best friends.

def check_best_friends(kids):
    for kid1 in kids:
        for kid2 in kids:
            if kid1 != kid2:
                print(f"{kid1} and {kid2} might be besties!")

❓ Time complexity?

✅ Answer: O(n²) — for each kid, check with every other kid.

🧩 Exercise 5: Magic Box 📦

You have a sorted list of stickers. You want to find "Unicorn" using binary search.

def find_unicorn(stickers):
    left, right = 0, len(stickers) - 1
    while left <= right:
        mid = (left + right) // 2
        if stickers[mid] == "Unicorn":
            return True
        elif stickers[mid] < "Unicorn":
            left = mid + 1
        else:
            right = mid - 1

❓ Time complexity?

✅ Answer: O(log n) — cut the box in half each time!

✨ Challenge Yourself:

Try to guess the Big O for these:

Reversing a list of n items
Adding an item to a dictionary
Looping twice one after the other (not nested)
Creating all possible pairs in a list
Looping inside a loop inside a loop (3 levels!)

Big-O Time Complexities Cheat Sheet

Category	Operation / Pattern	Time Complexity
Arrays	Traversal, updates	O(n)
	Merge Sort	O(n log n)
	Quick Sort (average)	O(n log n)
	Quick Sort (worst case)	O(n²)
	Binary Search	O(log n)
	Two-pointer / Sliding Window	O(n)
	Prefix Sum construction	O(n)
Strings	Reverse / Palindrome check	O(n)
	Hashmap-based Anagram check	O(n)
	Sorting-based Anagram check	O(n log n)
Linked List	Reverse (iterative / recursive)	O(n)
	Detect Cycle (Floyd’s)	O(n)
	Merge Two Sorted Lists	O(n)
	Find Middle Node	O(n)
Stack / Queue	Push / Pop / Enqueue / Dequeue	O(1)
	Valid Parentheses	O(n)
	Next Greater Element (Monotonic Stack)	O(n)
Hashing	Insert / Search / Delete (average)	O(1)
	Count Frequencies	O(n)
	Subarray with Sum / No Repeats	O(n)
Recursion / Backtracking	Subsets / Permutations	O(2^n * n)
Trees (Binary)	Traversals (Inorder / Pre / Post / Level)	O(n)
	Height, LCA, Validate BST	O(n)
Heaps	Insert / Delete (Min / Max heap)	O(log n)
	Build heap (heapify array)	O(n)
Dynamic Programming	0/1 Knapsack	O(n * W)
	Fibonacci (memoized)	O(n)
	Longest Common Subsequence (LCS)	O(n * m)
Graphs	DFS / BFS traversal	O(V + E)
	Detect Cycle (undirected)	O(V + E)
	Topological Sort	O(V + E)

Mastering Development Craftsmanship: A Comprehensive Guide to Essential Coding Practices

Ankush Singh Gandhi — Wed, 28 Feb 2024 01:08:04 +0000

Introduction

Welcome to the world of software development, where coding excellence is just the starting point. To truly thrive, developers must embrace a set of best practices that extend beyond functional code. In this comprehensive guide, we will explore a myriad of essential topics that every developer should focus on to build high-quality, maintainable, and efficient software.

1) Continuous Integration and Continuous Deployment (CICD)🔽

Implement tools like Jenkins or GitHub Actions for automated build, test, and deployment processes.
Accelerate development cycles by reducing manual intervention and minimizing the risk of errors.
CI/CD ensures changes are automatically tested and deployed in web applications, promoting collaboration.

2) Style Guide🔽

Follow coding style guides like PEP 8 for Python or Airbnb JavaScript Style Guide.
Consistent coding style enhances readability and maintainability.
Adherence to a style guide promotes code consistency across the entire codebase.

3) Document Code🔽

Utilize inline comments or tools like Sphinx and Javadoc for clear and concise documentation.
Documentation aids in understanding, maintaining, and extending code effectively.
Documenting REST API endpoints facilitates integration and helps in creating comprehensive API documentation.

4) Directory Structure🔽

Establish an organized directory structure for a well-maintained codebase.
Logical and consistent structure facilitates scalability and collaboration.
Group templates, static files, and views in separate directories for enhanced code readability in frameworks like Django.

5) Variable Naming🔽

Choose meaningful and descriptive variable names for improved readability.
Descriptive names reduce ambiguity and help developers understand the intent behind each variable.
Examples: userInput, totalRevenue, customerName instead of cname.

6) Comments🔽

Strive for self-explanatory code, but use comments for additional context.
Comments should be concise, accurate, and focused on explaining the "why" rather than the "what."
Commenting on complex algorithms or implementation choices enhances code clarity.

7) Justified File Names🔽

Use intuitive file names that reflect the content within, such as UserCard.jsx or apiService.js.
Meaningful file names contribute to overall code organization and facilitate navigation.
File names should not be arbitrary; they should provide a roadmap for fellow developers.

8) Keep it DRY (Don't Repeat Yourself)🔽

Reduce redundancy in code by encapsulating reusable logic in functions, classes, or modules.
DRY improves maintainability, consistency, and reduces the chance of errors.
Create utility functions for common calculations to ensure consistency.

9) KISS (Keep It Simple, Stupid)🔽

Strive for straightforward solutions that are easy to understand and maintain.
Choosing a simple, well-designed solution over unnecessary complexity enhances collaboration.
Opt for straightforward algorithms to improve readability and reduce the likelihood of introducing bugs.

10) Version Control🔽

Proper use of version control systems like Git is crucial for collaborative development.
Adopt branching strategies like Git Flow and follow commit best practices for a well-organized version history.
Resolve merge conflicts promptly and communicate changes effectively for a coherent version control history.

11) Testing🔽

Implement comprehensive testing, including unit tests, integration tests, and end-to-end tests.
Use tools like Jest for JavaScript or pytest for Python to ensure thorough test coverage.
A robust test suite acts as a safety net, catching bugs early and providing confidence for code changes.

12) Code Reviews🔽

Conduct effective code reviews with constructive feedback to maintain code quality.
Automated tools like linters complement manual code reviews, enhancing overall code quality.
Use a code review checklist to ensure thorough and consistent reviews across the team.

13) Security Best Practices🔽

Implement input validation, encryption, and regular security scanning to safeguard against vulnerabilities.
Prioritize security in the development lifecycle for building robust and secure applications.
Validate user inputs to prevent common security threats like SQL injection.

14) Performance Optimization🔽

Profile code using tools like cProfile or Chrome DevTools to identify bottlenecks.
Implement caching and select efficient algorithms for a responsive and scalable application.
Caching frequently accessed data reduces the load on databases and improves overall performance.

15) Error Handling🔽

Implement graceful error handling, logging, and monitoring with tools like Sentry or New Relic.
Maintain a robust error-handling strategy for enhanced user experience and simplified debugging.
Logging detailed error messages and stack traces aids in diagnosing issues quickly.

16) Dependency Management🔽

Effectively manage project dependencies for stability and security.
Specify version ranges, conduct security scanning, and consider license compatibility with tools like npm audit or FOSSA.
Regularly update dependencies to address security vulnerabilities for a secure codebase.

17) Code Refactoring🔽

Regularly refactor code to improve maintainability.
Identify code smells, apply refactoring techniques, and maintain test coverage during refactoring.
Tools like SonarQube assist in identifying areas for improvement.

18) Cross-Browser Compatibility🔽

Ensure web applications work across different browsers through comprehensive browser testing.
Use polyfills and responsive design techniques with tools like BrowserStack and Modernizr.
Testing on different browsers and devices guarantees a consistent user experience.

19) Accessibility🔽

Create accessible software using semantic HTML and ensuring keyboard accessibility.
Test with accessibility tools like Axe or Lighthouse for inclusivity.
Providing alternative text for images and ensuring proper focus order enhances accessibility.

20) Build Automation🔽

Automate the build process with tools like Gradle, Maven, or webpack.
Integrate build automation into continuous integration workflows for standardized development processes.
Automation streamlines tasks like compilation, minification, and packaging for a reliable build pipeline.

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Conclusion

By incorporating these essential coding practices into your development workflow, you are not just writing code; you are crafting software that is scalable, maintainable, and efficient. Continuously improving and adhering to these practices will not only benefit your current projects but will also elevate your skills as a proficient and sought-after developer in the ever-evolving landscape of software development. Remember, the journey to mastery is ongoing, and each best practice adopted is a step towards becoming a true development craftsman.

Python Roadmap 2024 🐍

Ankush Singh Gandhi — Fri, 09 Feb 2024 18:37:35 +0000

Python Developer Roadmap 2024

📌Step 1: Introduction🔽

Introduction to Python
- Brief history and origin of Python
- Guido van Rossum's role in Python's development
Python Environment Setup
- Installation of Python using different methods (Anaconda, virtual environments)
- Setting up an Integrated Development Environment (IDE) like VS Code, PyCharm
Features of Python
- Dynamic typing, simplicity, readability
- High-level programming language features
Basic Python Syntax
- Variables, data types, and expressions
- Understanding indentation and white space
Statements, Indentation, and Comments
- Proper use of indentation
- Comments and their importance
7 Reasons to Learn Python
- Versatility, community support, ease of learning
Benefits and Limitations of Python
- Use cases, strengths, and potential drawbacks
A Career in Python
- Job roles, industries, and demand for Python developers
Python vs Other Languages (Java, Scala, R)
- Comparison in terms of syntax, performance, and use cases
Applications of Python
- Web development, data science, artificial intelligence, automation
Compilers and Interpreters Available
- CPython, Jython, IronPython, PyPy
Getting to Know the Python Interpreter
- Basics of running Python code in the interpreter
Flavors of Python
- MicroPython, CircuitPython, IronPython

Step 2: Basics🔽

Python Variables
- Naming conventions, variable assignment
Python Variable Scope
- Local, global, and nonlocal scopes
Data Types in Python
- Integers, floats, strings, booleans
Python Operators
- Arithmetic, comparison, logical operators
Bitwise Operators
- Binary manipulation
Comparison Operators
- Detailed understanding of equality, inequality
Operator Precedence
- Order of execution in complex expressions
Ternary Operators
- Shorthand for conditional statements
Python Decision Making
- if, elif, else statements
Switch-case in Python
- Implementing switch-case using dictionaries
Loops in Python
- for and while loops
Numbers with Python
- Math module and its functions
Python Strings
- String manipulation, indexing, slicing
String Formatters and Escape Sequences
- Formatted strings, escape characters
String Functions and Operations
- Common string methods
The repr() Function
- Understanding the repr() function and its use

Step 3: Data Structures🔽

Python Lists
- List operations, methods, and built-in functions
List Comprehension
- Creating concise lists in a single line
The array Module
- Numeric arrays in Python
Python Tuples
- Immutable sequences and use cases
The zip() Function
- Merging multiple iterables
Tuples vs Lists
- Choosing the right data structure
Slicing in Python
- Extracting portions of sequences
Binary Sequence Types: bytes, bytearray, memoryview
- Dealing with binary data
Dictionaries in Python
- Key-value pairs, dictionary methods
Sets in Python
- Unordered collections with unique elements
Booleans in Python
- Logical data type

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Step 4: Functional Programming🔽

Functions in Python
- Defining functions, arguments, return values
Lambda Expressions
- Anonymous functions for concise code
Function Arguments
- Positional arguments, keyword arguments, default values
Function Recursion
- Solving problems using recursive functions
Built-in Functions
- Commonly used built-in functions
range()
- Generating sequences of numbers
eval()
- Evaluating dynamically created expressions
exec()
- Executing dynamic Python code
Decorators in Python
- Enhancing functions with decorators
Closure in Python
- Understanding closures and their applications
Working with itertools
- Powerful functions for iterators

Step 5: Modules and Packages🔽

Modules
- Creating and importing modules
Counter
- Counting occurrences in sequences
Defaultdict
- Default values for missing keys in dictionaries
OrderedDict
- Preserving order of dictionary items
namedtuple
- Creating named tuples for clearer code
Numeric Modules: math, decimal, random
- Mathematical operations, precision, random number generation
sys
- Accessing Python interpreter internals
Generating Random Numbers
- Using random module
Packages
- Creating and structuring packages
pip and PyPI
- Package management and external libraries
Modules vs Packages
- Differences and use cases

Step 6: Miscellany🔽

Regular Expressions with Python
- Pattern matching and manipulation
Multithreading in Python
- Parallel execution for improved performance
Working with Date and Time
- datetime module, formatting, time zones
Namespace and Scope
- Understanding variable scope and namespaces
Virtual Environments and Packages
- Managing dependencies with virtual environments
The datetime Module (Part I and II)
- In-depth exploration of date and time functions
The calendar Module
- Working with calendars and scheduling
The Python Debugger (pdb)
- Debugging tools and techniques
CGI Programming with Python
- Basics of Common Gateway Interface (CGI)
Understanding urllib
- Working with URLs and web requests
Terminologies in Python
- Glossary of key Python terms
What's new in Python 3.7?
- Updates and features introduced in Python 3.7
Deep Copy vs Shallow Copy
- Differences and use cases
Assert Statements in Python
- Using assertions for testing
Pretty-Printing with pprint
- Improved printing for complex data structures

Step 7: Object-Oriented Programming🔽

Methods in Python
- Defining methods within classes
Methods vs Functions
- Differences and use cases
Magic (Dunder) Methods
- Special methods for customization
Classes in Python
- Creating and using classes
Abstract class
- Defining abstract classes for inheritance
Concrete class
- Instantiating concrete classes
Python Objects
- Instances, attributes, and methods
Inheritance in Python
- Extending classes and reusing code
Multiple Inheritance
- Managing complexity with multiple base classes
Python Operator Overloading
- Customizing behavior for operators
Generators in Python
- Lazy evaluation for memory-efficient code
Iterators in Python
- Creating iterable objects
Generators vs Iterators
- Differences and use cases
Serialization with pickle
- Serializing and deserializing Python objects
The property Decorator
- Creating read-only and calculated attributes

Step 8: File Handling🔽

Reading and Writing Files
- Opening, reading, and writing to files
Managing Directories and Files
- Operations on directories and file paths
The os Module
- Interacting with the operating system
The shutil Module
- High-level file operations
Copying Files with Python
- Copying files and directories
Renaming Files with Python
- Changing file names programmatically
Zipping Files with Python
- Creating and extracting zip archives

Step 9: Exception Handling🔽

Errors and Exceptions
- Different types of errors in Python
Exception Handling
- Using try, except, finally blocks
Testing with unittest
- Unit testing for robust code

Step 10: Important Libraries🔽

Python Libraries
- Overview of key Python libraries and their applications
NumPy
- Numerical computing in Python
SciPy
- Scientific computing with additional functionality
pandas
- Data manipulation and analysis
Visualizing with matplotlib
- Creating various types of plots and visualizations
PyQT
- Developing desktop applications with Python
PyGTK
- GTK+ toolkit integration for Python
PyTorch
- Deep learning library
Altair
- Declarative statistical visualization library
Web Scraping with Scrapy
- Extracting data from websites
Data access mechanisms
- Connecting to databases, data storage
Spacy
- Natural Language Processing (NLP) library
pygame
- Game development with Python
tkinter
- GUI development toolkit

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Step 11: Other Functionality🔽

XML Processing
- Reading and writing XML files
Sending Mail with Python
- Sending emails programmatically
Networking
- Basics of network programming
Processing Images
- Working with images in Python
GUI Programming
- Developing graphical user interfaces
Forensics
- Basics of digital forensics with Python
Extensions to Python
- Exploring Python extension modules
Tools
- Popular Python development tools and IDEs
Accessing the Database
- Database connectivity and querying
Logging with Python
- Implementing logging for debugging and monitoring
Descriptors
- Understanding descriptors in Python
Buffering Protocol
- Managing input/output buffering
WSGI Protocol
- Web Server Gateway Interface for web applications
Context Managers
- Implementing and using context managers
Design Patterns
- Common design patterns in Python
Async.io
- Asynchronous I/O for concurrent programming
Metaprogramming
- Techniques for writing code that manipulates code

Project-Based Learning

Introduce project-based learning with practical Python projects.
Provide examples in web development, data analysis, and machine learning.

Best Practices and Code Style

Discuss Python best practices and adherence to coding standards.
Emphasize the importance of following PEP 8.

Testing and Test-Driven Development (TDD)

Introduce testing principles and the use of unittest and pytest.
Encourage the adoption of test-driven development.

Continuous Integration and Deployment (CI/CD)

Guide developers on setting up CI/CD pipelines using Jenkins, Travis CI, or GitHub Actions.

Containerization and Docker

Explore containerization with Docker for Python applications.
Cover Docker images, Docker Compose, and container orchestration.

Web Development Frameworks Beyond Django and Flask

Explore other web development frameworks like FastAPI, Tornado, and Pyramid.
Provide tutorials on building applications with these frameworks.

Database Connectivity and ORM

Extend the section on database access to include ORM with SQLAlchemy.
Discuss connecting to various databases and performing CRUD operations.

Advanced Topics in Data Science and Machine Learning

Dive deeper into advanced topics like deep learning with TensorFlow or PyTorch.
Explore NLP with spaCy and reinforcement learning.

Cloud Services and Deployment

Guide developers on deploying Python applications to cloud platforms (AWS, Azure, Google Cloud).
Discuss serverless computing, container orchestration, and cloud-native development.

Security Best Practices

Emphasize security best practices for Python applications.
Discuss common vulnerabilities, secure coding techniques, and code analysis tools.

Performance Optimization

Provide tips and techniques for optimizing Python application performance.
Discuss profiling tools, caching strategies, and code optimization.

Collaboration and Version Control

Discuss collaboration tools like Git and platforms such as GitHub or GitLab.
Explain branching strategies, pull requests, and code review best practices.

Community Engagement

Encourage readers to engage with the Python community.
Discuss participating in open-source projects, attending conferences, and joining forums.

Interview Preparation

Include a section on preparing for Python developer interviews.
Provide common interview questions, coding challenges, and tips for technical interviews.

Updates on Latest Python Releases

Regularly update the blog with information on the latest Python releases.
Highlight new features, improvements, and changes in best practices.

Step 12: Popular Frameworks🔽

Selenium
- Automation testing and web scraping
Web Frameworks
- Overview of various web frameworks
  - Django
    - Full-stack web development framework
  - Flask
    - Lightweight web framework for small to medium-sized applications

Step 13: Specializations🔽

Learn advanced Data Structures and Algorithms
- Advanced data structures (trees, graphs), algorithmic complexity
Metaprogramming
- Advanced techniques for code manipulation
Blockchain
- Understanding and implementing blockchain concepts
Quantum Programming
- Basics of quantum computing and programming
Artificial Intelligence & Deep Learning
- Neural networks, deep learning frameworks
Machine Learning
- Machine learning algorithms, model training
Data Science
- Exploratory data analysis, machine learning in data science
Ethical Hacking
- Cybersecurity, ethical hacking techniques

Step 14: Continuous Learning🔽

Stay updated with the latest Python versions and features
- Regularly check Python official documentation and release notes
Contribute to open-source projects
- Collaborate with the Python community on GitHub
Join developer communities
- Participate in forums, attend meetups and conferences
Attend conferences and webinars
- Stay informed about industry trends and best practices
Read Python-related blogs and articles
- Follow reputable sources for Python-related content

Step 15: Soft Skills Development🔽

Emphasize the importance of soft skills for Python developers.
- Effective communication, teamwork, and problem-solving skills
Discuss collaboration within development teams.
- Resources for improving teamwork and interpersonal skills
Provide resources for soft skills improvement.
- Books, courses, and workshops for enhancing communication and collaboration
Encourage community involvement.
- Engaging with local tech communities and online forums

Step 16: Version Control Systems Beyond Git🔽

Introduce other version control systems.
- Mercurial, SVN, and their use cases
Discuss strengths and weaknesses.
- When to prefer each version control system
Explore alternative workflows.
- Diverging and converging strategies in version control

Step 17: Python Design Patterns🔽

Dive into common design patterns in Python.
- Singleton, Factory, Observer, and more
Provide examples and use cases.
- Practical applications of each design pattern
Discuss best practices in design.
- Creating scalable and maintainable code

Step 18: Advanced Web Development🔽

Explore advanced web development concepts.
- Asynchronous web frameworks like FastAPI and Tornado
Introduce WebSocket communication.
- Real-time communication in web applications
Discuss server-sent events.
- Implementing real-time updates in web applications

Step 19: Real-time Applications with Python🔽

Explore building real-time applications.
- Technologies like WebSockets and asynchronous programming
Discuss frameworks suitable for real-time use cases.
- Applications in chat, notifications, and live updates

Step 20: Microservices Architecture🔽

Discuss the principles of microservices architecture.
- Decoupling and scalability in modern applications
Explore Python frameworks for microservices.
- Flask, FastAPI, and tools for building microservices
Discuss containerization and orchestration.
- Docker, Kubernetes, and managing microservices

Step 21: DevOps Practices🔽

Introduce DevOps practices relevant to Python development.
- Infrastructure as code (IaC), configuration management
Discuss continuous deployment.
- Implementing CI/CD pipelines with Jenkins, Travis CI, or GitHub Actions

Step 22: Advanced Data Processing🔽

Dive into advanced data processing techniques.
- Distributed computing with Apache Spark and Dask
Explore big data solutions.
- Handling large datasets efficiently

Step 23: IoT (Internet of Things) with Python🔽

Explore using Python for IoT projects.
- Connecting devices, data processing, and visualization
Discuss IoT libraries and frameworks.
- MicroPython, CircuitPython, and IoT platforms

Step 24: Quantum Computing with Python🔽

Provide an introduction to quantum computing principles.
- Basic concepts and principles of quantum programming
Explore Python libraries for quantum programming.
- Qiskit, Cirq, and tools for quantum computing

Step 25: Python for Automation and Robotics🔽

Discuss Python's role in automation and robotics.
- Libraries and frameworks for controlling robots and automating tasks
Explore real-world applications.
- Building automation scripts and controlling robots with Python

Step 26: Accessibility in Python Applications🔽

Highlight the importance of creating accessible software.
- Principles of accessibility and inclusivity
Discuss best practices for accessibility in Python applications.
- Implementing accessible user interfaces and content

Step 27: Contributing to Open Source🔽

Guide developers on how to contribute to open-source Python projects.
- Finding projects, submitting pull requests, and collaborating
Discuss the benefits of contributing to the community.
- Skill development, networking, and giving back

Step 28: Building a Portfolio🔽

Offer guidance on building a strong portfolio for Python developers.
- Showcasing projects, contributions, and skills effectively
Discuss the importance of a portfolio in job applications.
- Attracting employers and demonstrating expertise

Step 29: Python Career Paths🔽

Explore various career paths for Python developers.
- Roles in web development, data science, machine learning, DevOps, etc.
Discuss specialization and niche areas.
- Choosing a career path based on interests and skills

Step 30: Staying Current in Tech🔽

Provide tips on staying updated with the latest trends and technologies.
- Subscribing to newsletters, following industry blogs, and participating in online communities
Discuss the importance of continuous learning.
- Embracing a mindset of lifelong learning for career success

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Remember to adapt this roadmap based on your interests and career goals. The world of technology is constantly evolving, so staying curious and embracing a mindset of continuous learning is crucial for success. Happy coding!

Flutter With Python 😮😮

Ankush Singh Gandhi — Fri, 09 Feb 2024 17:25:40 +0000

In the dynamic landscape of mobile app development, the combination of Flutter and Python has gained considerable traction. This synergy allows developers to create cross-platform applications that marry the expressive UI capabilities of Flutter with the versatility and power of Python. In this guide, we'll delve into the integration of Flutter and Python using the Flet library, providing a detailed walkthrough from installation to creating a sample app and exploring more advanced scenarios.

Introduction to Flutter and Python Integration

1. Flutter: A Brief Overview:

What is Flutter? Flutter is an open-source UI software development toolkit created by Google. It enables developers to build natively compiled applications for mobile, web, and desktop from a single codebase.
Why Flutter? Flutter is known for its expressive UI, fast development cycles, and the ability to create beautiful, performant applications across multiple platforms.

2. Python in Mobile App Development:

Why Python? Python is renowned for its readability, ease of learning, and extensive libraries. Integrating Python into mobile app development allows developers to leverage its capabilities for data processing, machine learning, and more.

Getting Started with Flet

1. What is Flet?

Flet is a Python package that acts as a bridge between Flutter and Python. It facilitates seamless communication, enabling data exchange and method invocation.

2. Installing Flet:

Use pip to install Flet in your Python environment: bash pip install flet

3. Setting Up a Flutter Project:

Install Flutter by following the official documentation: Flutter Installation Guide
Create a new Flutter project using: bash flutter create my_flutter_project

4. Connecting Flutter with Python using Flet:

Initialize Flet in your Flutter project by adding the Flet dependency in your pubspec.yaml file: yaml dependencies: flet: ^0.2.1

Creating a Sample Flutter-Python App

1. Building the Flutter UI:

Create a simple Flutter UI with a button that triggers a Python function.

Example Flutter code (lib/main.dart):

 import 'package:flutter/material.dart';
 import 'package:flet/flet.dart';

 void main() {
   runApp(MyApp());
 }

 class MyApp extends StatelessWidget {
   @override
   Widget build(BuildContext context) {
     return MaterialApp(
       home: MyHomePage(),
     );
   }
 }

 class MyHomePage extends StatelessWidget {
   @override
   Widget build(BuildContext context) {
     return Scaffold(
       appBar: AppBar(
         title: Text('Flutter with Python'),
       ),
       body: Center(
         child: ElevatedButton(
           onPressed: () {
             Flet.callPythonFunction('my_python_function', {'param': 'Hello from Flutter!'});
           },
           child: Text('Invoke Python Function'),
         ),
       ),
     );
   }
 }

2. Handling Python Function in Flutter:

Implement the corresponding Python function using Flet.

Example Python code (my_python_script.py):

 from flet import Flet

 @Flet.register_function
 def my_python_function(param):
     print(f'Received from Flutter: {param}')
     # Add your Python logic here

3. Running the Flutter App:

Launch your Flutter app using: bash flutter run

4. Running the Python Script:

Run your Python script using: bash python my_python_script.py

Advanced Integration: Using Python for Data Processing

1. Integrating Python Data Processing:

Leverage Python's data processing capabilities within your Flutter app.

Example Python code (my_data_processing.py):

 from flet import Flet

 @Flet.register_function
 def process_data(data):
     # Add your data processing logic here
     result = [item.upper() for item in data]
     return result

2. Handling Data Processing in Flutter:

Modify your Flutter app to handle data processing using Python.

Example Flutter code (lib/main.dart):

 class MyHomePage extends StatelessWidget {
   List<String> items = ['apple', 'banana', 'orange'];

   @override
   Widget build(BuildContext context) {
     return Scaffold(
       appBar: AppBar(
         title: Text('Flutter with Python'),
       ),
       body: Center(
         child: Column(
           mainAxisAlignment: MainAxisAlignment.center,
           children: <Widget>[
             ElevatedButton(
               onPressed: () async {
                 List<dynamic> processedData =
                     await Flet.callPythonFunction('process_data', {'data': items});
                 print('Processed Data: $processedData');
               },
               child: Text('Process Data with Python'),
             ),
           ],
         ),
       ),
     );
   }
 }

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Conclusion

Integrating Flutter with Python using the Flet library provides developers with a flexible and powerful solution for cross-platform development. From invoking Python functions to leveraging Python's data processing capabilities within your Flutter app, this integration opens up a world of possibilities. As you explore and apply these techniques in your projects, you'll find that the combination of Flutter and Python brings together the best of both worlds. Happy coding!

Financial Data with Python 🤑🤑

Ankush Singh Gandhi — Fri, 09 Feb 2024 16:59:25 +0000

In the fast-paced world of finance, having access to real-time market data is crucial for making informed decisions. Global Data Feeds (GDF) provides a powerful WebSocket API that enables developers to tap into a continuous stream of financial data. In this blog, we'll explore how to use the GDF WebSocket API with Python to fetch and process real-time financial information.

Understanding Global Data Feeds WebSocket API

1. Introduction to Global Data Feeds:

Global Data Feeds Platform: A comprehensive platform offering financial market data from various exchanges.
WebSocket API: A real-time communication protocol for efficient and continuous data updates.

2. Key Features of GDF WebSocket API:

Real-Time Price Updates: Receive live updates on stock prices, indices, and other financial instruments.
Market Depth Information: Access detailed market depth information for better analysis.
Trade Execution Updates: Stay informed about trade executions and transactions.

Getting Started with Python and GDF WebSocket API

1. Setting Up Your GDF Account:

Register on GDF: Sign up for an account on the Global Data Feeds platform.
API Access Credentials: Obtain API access credentials (API key, secret key).

2. Installing Required Python Libraries:

Use pip to install necessary libraries: bash pip install websocket-client requests

3. Connecting to GDF WebSocket API:

Use the websocket-client library to establish a WebSocket connection.
Implement authentication using your GDF API key.

   import websocket

   import json

# Replace with your GDF API key

   API_KEY = "your_api_key"

def on_message(ws, message):

       data = json.loads(message)

       # Process incoming data as per your requirements

       print(data)

def on_error(ws, error):

       print(f"Error: {error}")

def on_close(ws, close_status_code, close_msg):

       print("Closed")

def on_open(ws):

       auth_payload = {"event": "auth", "apiKey": API_KEY}

       ws.send(json.dumps(auth_payload))

if name == "main":

       ws_url = "wss://api.gdf.com/ws"

       ws = websocket.WebSocketApp(ws_url, on_message=on_message, on_error=on_error, on_close=on_close)

       ws.on_open = on_open

       ws.run_forever()

Exploring Real-Time Financial Data

1. Receiving Price Updates:

Subscribe to specific instruments to receive real-time price updates.
Extract and process price information for analysis.

   def on_open(ws):

       # Subscribe to real-time updates for AAPL and MSFT stocks

       subscribe_payload = {"event": "subscribe", "ticker": ["AAPL", "MSFT"]}

       ws.send(json.dumps(subscribe_payload))

# ...



   # Process real-time price updates

   def process_price_update(data):

       instrument = data["ticker"]

       price = data["price"]

       print(f"Price Update for {instrument}: ${price}")

Accessing Market Depth Information:

Subscribe to market depth updates for a specific instrument.
Extract and analyze market depth data for better insights.

   def on_open(ws):

       # Subscribe to market depth updates for AAPL stock

       depth_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL"], "depth": 5}

       ws.send(json.dumps(depth_subscribe_payload))

# ...



   # Process market depth updates

   def process_market_depth(data):

       instrument = data["ticker"]

       bids = data["bids"]

       asks = data["asks"]

       print(f"Market Depth for {instrument} - Bids: {bids}, Asks: {asks}")

Handling Trade Execution Updates:

Subscribe to trade execution updates for a specific instrument.
Capture and process trade execution data for timely information.

   def on_open(ws):

       # Subscribe to trade execution updates for AAPL stock

       trade_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL"], "trades": True}

       ws.send(json.dumps(trade_subscribe_payload))

# ...



   # Process trade execution updates

   def process_trade_execution(data):

       instrument = data["ticker"]

       price = data["price"]

       quantity = data["quantity"]

       print(f"Trade Executed for {instrument} - Price: ${price}, Quantity: {quantity}")

Advanced Usage of Global Data Feeds WebSocket API

1. Fetching Last Quotes for Multiple Instruments:

Extend the WebSocket subscription to receive last quotes for multiple instruments simultaneously.
Parse and process the last quote data for detailed insights.

   def on_open(ws):

       # Subscribe to last quotes for AAPL, MSFT, and GOOGL stocks

       last_quote_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL", "MSFT", "GOOGL"], "lastquote": True}

       ws.send(json.dumps(last_quote_subscribe_payload))

# ...



   # Process last quote updates

   def process_last_quote(data):

       instrument = data["ticker"]

       bid_price = data["bid"]

       ask_price = data["ask"]

       print(f"Last Quote for {instrument} - Bid: ${bid_price}, Ask: ${ask_price}")

Fetching Strike Prices for Options:

Subscribe to receive information about strike prices for options on a particular underlying instrument.
Analyze and utilize strike price data for option trading strategies.

   def on_open(ws):

       # Subscribe to strike prices for options on AAPL

       strike_price_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL"], "strikeprices": True}

       ws.send(json.dumps(strike_price_subscribe_payload))

# ...



   # Process strike price updates

   def process_strike_price(data):

       instrument = data["ticker"]

       strike_prices = data["strikeprices"]

       print(f"Strike Prices for Options on {instrument}: {strike_prices}")

Fetching Instrument Details:

Retrieve detailed information about a specific financial instrument.
Utilize instrument details for comprehensive market analysis.

   def on_open(ws):

       # Subscribe to instrument details for AAPL

       instrument_details_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL"], "instrumentdetails": True}

       ws.send(json.dumps(instrument_details_subscribe_payload))

# ...



   # Process instrument details

   def process_instrument_details(data):

       instrument = data["ticker"]

       details = data["instrumentdetails"]

       print(f"Instrument Details for {instrument}: {details}")

Fetching Last Quote Array:

Subscribe to receive an array of last quotes for multiple instruments.
Handle and process the array of last quotes for efficient data analysis.

   def on_open(ws):

       # Subscribe to last quotes array for AAPL, MSFT, and GOOGL stocks

       last_quote_array_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL", "MSFT", "GOOGL"], "lastquotearray": True}

       ws.send(json.dumps(last_quote_array_subscribe_payload))

# ...



   # Process last quote array updates

   def process_last_quote_array(data):

       quotes_array = data["lastquotes"]

       for quote in quotes_array:

           instrument = quote["ticker"]

           bid_price = quote["bid"]

           ask_price = quote["ask"]

           print(f"Last Quote for {instrument} - Bid: ${bid_price}, Ask: ${ask_price}")

Fetching Expiry Dates for Options:

Subscribe to receive expiration dates for options on a particular underlying instrument.
Leverage expiration date information for options trading strategies.

   def on_open(ws):

       # Subscribe to expiration dates for options on AAPL

       expiry_dates_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL"], "expirydates": True}

       ws.send(json.dumps(expiry_dates_subscribe_payload))

# ...



   # Process expiry date updates

   def process_expiry_dates(data):

       instrument = data["ticker"]

       expiry_dates = data["expirydates"]

       print(f"Expiry Dates for Options on {instrument}: {expiry_dates}")

Fetching Option Chain Data:

Subscribe to receive the option chain data for a specific underlying instrument.
Analyze the option chain for various strike prices, expirations, and Greeks.

   def on_open(ws):

       # Subscribe to option chain data for AAPL

       option_chain_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL"], "optionchain": True}

       ws.send(json.dumps(option_chain_subscribe_payload))

# ...



   # Process option chain updates

   def process_option_chain(data):

       instrument = data["ticker"]

       option_chain = data["optionchain"]

       print(f"Option Chain for {instrument}: {option_chain}")

Fetching Greeks for Options:

Subscribe to receive Greeks (Delta, Gamma, Theta, Vega) for options on a specific underlying instrument.
Utilize Greeks data for risk management and option pricing strategies.



def on_open(ws):

       # Subscribe to Greeks for options on AAPL

       greeks_subscribe_payload = {"event": "subscribe", "ticker": ["AAPL"], "greeks": True}

       ws.send(json.dumps(greeks_subscribe_payload))

# ...



   # Process Greeks updates

   def process_greeks(data):

       instrument = data["ticker"]

       greeks_data = data["greeks"]

       print(f"Greeks for Options on {instrument}: {greeks_data}")

Best Practices and Considerations

1. Error Handling and Reconnection:

Implement robust error handling to manage unexpected scenarios.
Set up reconnection mechanisms to ensure continuous data flow.

2. Data Processing and Analysis:

Use appropriate data structures to efficiently process and store real-time data.
Implement algorithms for analyzing price trends, market depth, and trade executions.

3. Throttling and Rate Limiting:

Adhere to GDF's API usage policies, including any rate limits imposed.
Implement throttling mechanisms to avoid exceeding rate limits.

Advanced Real-Time Financial Applications

1. Building an Advanced Options Trading Dashboard:

Utilize option chain data, strike prices, and Greeks for creating a comprehensive options trading dashboard.
Implement real-time updates for efficient decision-making.

2. Implementing Algorithmic Trading Strategies:

Leverage real-time data, last quotes, and instrument details to implement algorithmic trading strategies.
Integrate decision-making logic based on advanced financial data.

3. Developing a Real-Time Stock Ticker:

Utilize GDF WebSocket API to create a dynamic stock ticker application.
Display real-time price updates for selected instruments.

4. Building a Market Depth Analyzer:

Implement a tool to visualize and analyze market depth data.
Use graphical representations to enhance understanding.

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Conclusion

Congratulations! You've explored advanced features of the Global Data Feeds WebSocket API with Python, enabling you to build sophisticated financial applications. By incorporating last quotes, strike prices, instrument details, option chain data, and Greeks into your analysis, you can take your financial insights to the next level. Happy coding and profitable trading!

Part 2 Coming Soon.......

Creat Algorithm Complexity Chrome Extension

Ankush Singh Gandhi — Sat, 01 Oct 2022 14:53:33 +0000

If you are a Google Chrome user, you've probably used some extensions in the browser.

Have you ever considered building one yourself? In this tutorial, I'll show you how to build a Chrome extension from the ground up.

What is a Chrome Extension?

A chrome extension is an application that is installed in the Chrome browser to improve the browser's capabilities. You can easily create one by utilising web technologies like as HTML, CSS, and JavaScript.

Developing a chrome extension is similar to creating a web application, but it requires a manifest.json file, which we will go over in the last of this post.

What Will our Chrome Extension Look Like?

The complete source code of this project can be found on GitHub.

How to Create a Chrome Extension?

Let's dive into the step-by-step process:

1. Create Project Folder and HTML File

Start by creating an empty folder for your project. Inside it, create an index.html file with the following HTML boilerplate:

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">

    <link rel="stylesheet" href="popup.css">

    <title>Algoritm Complexity Extension</title>
</head>

<body>
</body>

<script src="popup.js"></script>
</html>

2. Design Your Extension Popup

In the popup.html file, structure your extension's popup:

<body>
    <h1 class="ml title">Algoritm Complexity Extension</h1>

    <div class="container">
        <h3 class="algo" id="algorithm"></h3>
        <p><b>Best time complexity: </b><div id="best"></div></p>
        <p><b>Average time complexity: </b><div id="avg"></div></h4>
        <p><b>Worst time complexity: </b><p id="worst"></p></h4>
        <div class="al-auto">
            <button id="algo" class="button">Next</button>
        </div>
    </div>
    <p class="ml">
        &copy; <a href="https://github.com/ankushsinghgandhi" style="text-decoration: none; color:blue;">Ankush Singh Gandhi</a> 2021
    </p>
</body>

3. Style Your Popup

In popup.css, style your popup to make it visually appealing:


body {
    width: 300px;
    background-color:cornsilk;
}
.ml {
    margin: auto;
    text-align: center;
}
.title {
    text-align: center;
}
.container {
    height: 280px;
    position: relative;
    border: 3px solid green;
}
.al-auto {
    margin: 0;
    position: absolute;
    bottom: 10%;
    left: 50%;
    -ms-transform: translate(-50%, -50%);
    transform: translate(-50%, -50%);
}

.button {
    background-color: #4CAF50; /* Green */
    border: none;
    color: white;
    padding: 5px;
    text-align: center;
    text-decoration: none;
    display: inline-block;
    font-size: 16px;
    font-weight: bold;
}

4. Implement JavaScript Logic

In popup.js, add JavaScript code to handle button clicks and display algorithm complexities:

var algorithms = ['Linear Search','Binary Search', 'Bubble Sort', 'Selection Sort', 'Insertion Sort', 'Merge Sort', 'Quick Sort', 'Heap Sort', 'Bucket Sort', 'Radix Sort', 'Tim Sort', 'Shell Sort']
var besttime = ['O(1)', 'O(1)', 'O(n)', 'O(n^2)', 'O(n)', 'O(nlogn)', 'O(nlogn)', 'O(nlogn)', 'O(n+k)', 'O(nk)', 'O(n)', 'O(n)']
var avgtime = ['O(n)', 'O(logn)', 'O(n^2)', 'O(n^2)', 'O(n^2)', 'O(nlogn)', 'O(nlogn)', 'O(nlogn)', 'O(n+k)', 'O(nk)', 'O((nlogn)^2)']
var worsttime = ['O(n)', 'O(logn)', 'O(n^2)', 'O(n^2)', 'O(n^2)', 'O(nlogn)', 'O(n^2)', 'O(nlogn)', 'O(n^2)', 'O(nk)', 'O((nlogn)^2)']

document.getElementById("algo").addEventListener("click", newComplexity);

function newComplexity() {
    var random = Math.floor(Math.random() * (algorithms.length));
    document.getElementById('algorithm').innerText = algorithms[random];
    document.getElementById('best').innerHTML = besttime[random];
    document.getElementById('avg').innerHTML = avgtime[random];
    document.getElementById('worst').innerHTML = worsttime[random];
}

newComplexity()

5. Create manifest.json

In the root folder, create a manifest.json file:

{
    "manifest_version": 3,
    "name": "Algoritm Complexity Extension",
    "version": "1.0.0",
    "description": "Shows complexities of random algorithms",
    "action": {
        "default_popup": "popup.html",
        "default_icon": "algo-icon.png"
    },
    "icons": {
        "16": "algo-icon.png",
        "32": "algo-icon.png",
        "48": "algo-icon.png",
        "128": "algo-icon.png"
    }
}

And add the following code inside the file and save.

Name, version, description, manifest version (3 in this example, which is the most recent manifest version), author, and action columns are all included in our manifest.json file. The value for default popup in the action field contains the location to the HTML file, which in this example is index.html.

You can examine all of the manifest.json file configurations here.

6. Load the Extension in Chrome

We need to go to Select More Tools and then choose Extensions from the browser menu as shown in the picture below:

After choosing Extensions, it redirects to the extensions page in Chrome. Make sure to enable the Developer mode here.

Once that's done, you need to click the Load unpacked button which will allow us to load our project in the Chrome extension store.

Now, the extension is available in our Chrome extension store. You can also pin the extension in the browser.

This extension works only in your browser. If you want to publish it on the Chrome Web Store, you can follow this link.

CONGRATULATIONS, we just created our very first chrome extension.

The entire code for this project is available on Github.

I hope this post help you get your first chrome extension up and running. if you need help in creating a chrome extension, or need help with any other project you can DM me on twitter

There more similar projects available on my GitHub.

Understanding Software Engineering

Ankush Singh Gandhi — Thu, 20 May 2021 06:10:21 +0000

Software Engineering🔽

It is a systematic, disciplined, cost-effective technique for software development.
It is an Engineering approach to develop software.

🟢Software Engineering Process

1) Software Development Lifecycle
2) Requirements & Specification (SRS)
3) Architecture
4) Software Design Process
5) Implementation
6) Development
7) Testing

Software Development Lifecycle🔽

It is a process used by the software engineering industry to design develop and test high-quality software SDLC tends to produce high-quality software that meets customer expectations.

🟢SDLC Models

There are processes, methodologies and frameworks range from specific prescriptive steps that can be used by the organization there are many SDLC models that have been developed to achieve the different required objective the model specifies various stages of the process and the order in which they are carried out.

Waterfall Model
V Model
Incremental Model
Iterative Model
Spiral Model
RAD Model
Prototype Model
Agile Model

🟢Waterfall Model

It is a breakdown of project activities into linear sequential phases where each face depends on the deliverables of the previous one.

🟢Incremental Model

The incremental build model is a method of software development where the model is designed, implemented, and tested incrementally (a little more is added each time) until the product is finished the product defined finished when it satisfies all requirements

Each iteration passes through the requirement, design, coding, and testing phase and each subsequent release of the system adds function to the previous release until all designed functionality has been implemented.

🟢Iterative Model

The iterative model is a particular implementation of a software development life cycle (SDLC) that focuses on an initial, simplified implementation, which then progressively gains more complexity and a broader feature set until the final system is complete. When discussing the iterative method, the concept of incremental development will also often be used liberally and interchangeably, which describes the incremental alterations made during the design and implementation of each new iteration.

Unlike the more traditional waterfall model, which focuses on a stringent step-by-step process of development stages, the iterative model is best thought of as a cyclical process. After an initial planning phase, a small handful of stages are repeated over and over, with each completion of the cycle incrementally improving and iterating on the software. Enhancements can quickly be recognized and implemented throughout each iteration, allowing the next iteration to be at least marginally better than the last.

🟢Spiral Model

The spiral model combines the idea of iterative development with the systematic, controlled aspects of the waterfall model. This Spiral model is a combination of the iterative development process model and sequential linear development model i.e. the waterfall model with a very high emphasis on risk analysis. It allows incremental releases of the product or incremental refinement through each iteration around the spiral.

🟢Agile Model

The meaning of Agile is swift or versatile." Agile process model" refers to a software development approach based on iterative development. Agile methods break tasks into smaller iterations or parts that do not directly involve long-term planning. The project scope and requirements are laid down at the beginning of the development process. Plans regarding the number of iterations, the duration, and the scope of each iteration are clearly defined in advance.

Each iteration is considered as a short time "frame" in the Agile process model, which typically lasts from one to four weeks. The division of the entire project into smaller parts helps to minimize the project risk and to reduce the overall project delivery time requirements. Each iteration involves a team working through a full software development life cycle including planning, requirements analysis, design, coding, and testing before a working product is demonstrated to the client.

Agile is a methodology. It's a way of thinking. It is NOT a model in and of itself.

Agile was invented because the scope of software development was changing. Waterfall methods aren't bad, they're just slow, and not built for a lot of modern problems.

This is largely due to the complexity of modern systems which make them hard to plan 100%. This means at some point, a change will be required, and production will stop until all the documents are updated.

With agile however, we can move a little bit quicker. We are constantly looking for and adjusting to change.

📌Agile Manifesto

The agile manifesto is a set of guiding principles for agile development. They are:

🔺Individuals and interactions over process and tools.

🔺Working software over comprehensive documentation.

🔺Customer collaboration over contract negotiation.

🔺Responding to change over following a plan.

With this set of rules, all agile models are created. Note, in here we aren't throwing out processes and tools, and we aren't going without a plan. We are simply just creating priorities within the development process. We want to make the piece of software that is needed. To do this, we need to keep communication channels open and collaborate with all involved.

Through this manifesto, models were created that fit these rules.

📌Agile Models

🔺Scrum - Scrum is focused on sprints. Sprints are these 1-4 week production cycles. We take the software, come up with a goal of where we want it to be, and then build it to there.

Once we finish a sprint, we then go back to the stakeholders, show them the software, take suggestions, and move on to the next sprint.

All of this allows us to stay flexible. We are communicating with the stakeholders almost every 2 weeks with this model. This means we are constantly able to take those suggestions and change the direction of development.

For more deeper understanding

Scrum Cheat Sheet

Ankush Singh Gandhi ・ Feb 4 '21

🔺Kanban - The kanban system is one of optimization. With kanban, we are trying to analyze the flow of production and figure out the slowdowns.

To do this, we usually use some sort of visual flowchart. We break the project up into tasks and fill up the chart. We are then able to see if any part of production has a slowdown. Maybe for example, our review process is slowing us down, or maybe it's planning.

With kanban, we are trying to make small adjustments into the right direction. We want to work with the existing process, not replace it.

🔺Lean Startup - Lean startup is a way of testing out the market before spending on development. Here we create a MVP (Minimum Viable Product) to see if there is interest in the product we are developing.

Production costs a lot of money. It would be really bad if we spent $500,000 on a project, just to figure out that nobody is actually interested in that product.

An example of this would be to build a website that sells a certain product. Get it working to the point where people can place that item into a cart. Then when they go to purchase, have it give them a friendly message stating that this feature will be coming soon. We then track how many people are actually interested in buying products off our website.

If we have a lot of interest, then we are good to go ahead with production. If we don't have as much interest, then maybe we need to rethink our design.

🟢Waterfall Model Example

1) Requirements

collecting email & message
store in db
prevent bad input

2) Design

using HTML & CSS for building
JS for input verification
MySQL for backend

3)Development

actual coding
documenting

4) Testing

does form collect input
does it send info. to db
does it prevent bad input

5) Deployment

deploying on AWS

6) Maintainence

fixing bugs
adding features

Requirements & Specification🔽

🟢Requirements

A way of figuring out the exact specification of what the software should do all things that define the goal of the system.

A non-tech definition of something the user requires from the system.
Anyone can understand.
ex - ability to submit a request for medical treatment form.

🟢Specification

A more technical way of figuring out exactly what the software should do we can say it is the technical aspect of requirements.

A technical definition of what users require from the system.
Keep it simple we are not trying to design.
ex - send AES 256 form data from the frontend to the server.

🟢Example

Requirement

The tire must work on SUV automobiles.

Specification

Must support 75,00lbs pressure.
Tire must fill US DOT standards.
T or greater speed quality.

🟢Types of Requirements

On the basis of functionality there are two types of requirements

1.Functional

What are the functions of the program
what should the system do

2.Non-Functional

what goals should be met
How should the system work

Other than these two there are three more types of requirements

1.Product

must have of the product itself
ex - the app must be coded in kotlin

2.Organizational

company policies, standards, style, etc
ex - data should be encrypted by AES 256
ex - project should be developed by SCRUM

3.External

external law, regulations, trends, etc
ex - it must use SSL due to law Xyz in the US

Design: Architecture🔽

🟢Architecture Introduction

Architecture is the highest level of design within a system. It is the link between idea and reality. It takes our idea for the system, and creates a plan for it. We focus on only the largest areas of the system here. We want to break it down from idea, into concrete areas to build.

In software, bad architecture is something that CAN'T be fixed with good programming. It is a critical step within the development process. Once we decide on an architecture, we have to understand that it can't be changed.

🟢Architecture Overview

📌Bad Architecture

📌Good Architecture

Software architecture is all about breaking up larger systems and ideas, into smaller focused systems. Our first step is to take the requirements, and build an initial architecture. We take this broad set of ideas and guidelines, and have to organize it into functioning areas.

Each of these areas are then put through the same process to break them up into smaller and smaller pieces. Eventually we will have a blueprint for the entire system designed.

Good architecture is hard. It takes a lot of resources to develop correctly. However, this upfront cost is almost always recovered from how maintainable the software is. This will reduce the amount of bugs, and the time to fix those bugs.

Good architecture also helps for faster development and better resource utilization. If we break up the project into small pieces, we will understand how to have multiple developers work at the same time on it.

🟢Software Architecture example

1.We are going to design an architecture of a game.

2.now we should divide these layers based on functionality.

3.now we will add another layer that controls the interaction between frontend and game logic.

🟢Architecture Patterns

📌Pipe-and-Filter

The pipe and filter pattern is a good pattern to use to process data through multiple different layers. The key to this pattern is the ability of each step to input, and output the same type of data. So if you send a set of numbers in one side, you will get a set of numbers out the other side.

🟢 Architecture Patterns

📌 Pipe-and-Filter

The pipe and filter pattern is a good pattern to use to process data through multiple different layers. The key to this pattern is the ability of each step to input and output the same type of data. So if you send a set of numbers in one side, you will get a set of numbers out the other side.

This key constraint makes it so you can mix and match the logic in any order and still have the program work. These different filters can also be set up across multiple servers.

There is definitely an added complexity with this pattern. Setting it up can be tricky to get correct. Also, if the data is lost at any step, the entire process is broken.

📌 Client-Server

The client-server pattern is one that is quite common today. Every single website and most phone apps use this architecture. With this pattern, there are two parts to the software, the client, and the server.

Software Engineering

Let's take an iPhone app, for example. What you download in the app store is what is known as the "client software". This is the version of the app built to talk to the server. It doesn't store any of the server's data locally. It is just set up to make the appropriate server calls when necessary.

The other part of this is, of course, the "server software". This is the software that is installed onto a server to receive the requests from the client. The server holds and updates the data. It also processes requests and sends the data to the clients. Servers have to be tuned correctly, as there can be a near unlimited number of clients requesting information.

This is a great pattern for accessing and updating a single repository of information. It's great for keeping track of accounts and regulating which data is given automatically.

📌 Master-Slave

The master-slave pattern consists of two elements, the master, and the slave. The master is in full control of all slaves associated with it. This is good for a multitude of different applications.

Software Engineering

One such application is with duplicate backup servers. You don't want these backup servers all acting independently of one another. This will create a bunch of different states of memory. Each server will have a different set of data. Instead, you have a master server, which is the main server of operation.

The master server is the one dealing with all of the normal day-to-day operations. Then at some point during the day, it sends a signal out to all of the slave servers to tell them to begin their backup operation. The slave servers all start up, copy the data from the master server, and then go back to sleep.

This pattern is also used with "multi-threading". Here we break up an operation into a bunch of small parts. Each of those parts is given a thread and fed through the CPU. If a CPU has multiple cores, it can process multiple threads at the same time.

We typically have a master thread that controls the creation and tracking of all slave threads. The slaves do exactly what the master thread has told them. The master thread keeps reassessing the situation both creating and deleting slave threads. Once the operation is finished, the master thread ceases as well.

📌 Layered Pattern

The layered pattern consists of divvying up the program into layers of technology. These layers only communicate with adjacent layers. Let's say we have an architecture with 9 layers. In this model, 8 would only be able to communicate with 9 and 7, 4 with 3 and 5, etc.

Software Engineering

This simplifies the communication channels and helps to better distinguish the areas of the program. Overall, this helps to make the program more maintainable. The downside to this is that there can be added complexity in some areas. For example, if you need to send a message from layer 1 to layer 9, there will have to be a function in each layer to pass that message along.

🟢 Architecture Conclusion

There is no one-size-fits-all plan when it comes to software development. The process must be taken on a case-to-case basis. Us, as engineers, seek to find the best pattern or set of patterns that solve the problem.

This process is an iterative one. We come up with an idea, get feedback, rework it, and repeat the process many times. After a series of iterations, we have the architecture that will work best for the problem.

Design: Modularity🔽

The design is where we really plan out our system. We can go as detailed as possible in this step.

The main focus here is to break up the project into subsystems and modules.

Subsystem: Independent system that holds independent value.
Module: Component of a subsystem that cannot function as a standalone.

We should slowly refine and reorganize the systems and sub-modules until they make the most sense.

Software Engineering

Design is two things:

Activity: Working to design the software.
Product: A document or set of documents detailing the design of the software.

Information Hiding - Hiding the complexity of the program inside of a "black box".

Data Encapsulation - Hiding the implementation details from the user, providing only an interface.

Both of these work to hide the implementation details and protect the integrity of the data. We want to control the flow of data and provide the user with an easy-to-use experience.

With these ideas, we can take the most complex of code and make it accessible to anyone. Doing this at each step in the design process also helps make the code easier to maintain.

With it implemented properly, we don't need to know the entire codebase to make a change. We only need to know the part of the program we are working on. The encapsulation of all the other levels of the program makes things easy to test and change.

🟢 Goals of Modularity

1) Abstraction
2) Composability
3) Decomposability
4) Protectibility
5) Continuity
6) Module understandability

🟢 Coupling Introduction

Coupling is one of the major things to look at when designing the modularity of the system. It details how dependent each module is on every other module. A set of modules with tight coupling is bad design. It creates hard-to-maintain code.

We don't want our modules to be dependent on one another. We want to be able to swap one module out with another and only have to update code in the swapped module. The more dependent our program is, the more and more modules we will have to rewrite for every change.

📌 Tight Coupling

This is the worst form of coupling. Tight coupling means there is a strong dependence between modules. Changes will be very hard to make, and bugs will be difficult to track down.

Content Coupling: When one module modifies or relies on the internal working of another module.
Common Coupling: When several modules have access to the same global data.
External Coupling: When several modules have direct access to the same external Input/Output.

📌 Medium Coupling

Here the coupling is getting better, but we still have room for improvement.

Control Coupling: This is when data is passed that influences the internal logic of another module.
Data Structure Coupling: This is when multiple modules share the same data-structure.

📌 Loose Coupling

Data Coupling: This is when two modules share the same data. This is a good form of coupling.
Message Coupling: This coupling is when messages or commands are passed between modules.
No Coupling: No communication between modules whatsoever.

🟢 Cohesion Introduction

Cohesion is the other area to focus on when we are talking about modularity. Cohesion is the measurement of how focused our module is on a single task. The more focused the module, the higher the cohesion.

With cohesion, higher is better. We want modules that only do one thing and one thing only. The reason for this is with maintainability.

What we want to find is the point where we can maximize the effects of both. We want to create loose coupling and high cohesion.

📌 Weak Cohesion

Coincidental Cohesion: The tasks within the module are only linked because they are in the same module. This is the weakest form of cohesion. Here, the modules are completely random. There is nothing linking the tasks within a module, except the fact that they were simply put into the same file.
Temporal Cohesion: The tasks within the module are only linked because events happen around the same time.
Logical Cohesion: The tasks within the module are linked due to being in the same general category.

📌 Medium Cohesion

Procedural Cohesion: The order of execution passes from one command to the next. Here we have a relationship of time. This is different from temporal because the tasks are both linked and essential for one another. There is an order by which these must be executed to work properly.
Communicational Cohesion: When all tasks support the same input and output data.
Sequential Cohesion: A combination of the previous two. When all tasks work in which the output data for one is the input data for the next. With this, we have a procedure of tasks, and these tasks all share the same data.

📌 Strong Cohesion

Functional Cohesion: This is when all tasks within a module support activities needed for one and only one problem-related task. In essence, the module only does a single action.
Object Cohesion: This can either be lumped in with functional cohesion or by itself. Object cohesion is when all activities modify a single object.

This only works in object-oriented languages. An example might be a module that only modifies a user object. All tasks within this module update the user module in some way.

🟢 Cohesion Conclusion

Cohesion is really important to make code that is easy to understand and maintain. The more focused the modules, the easier the code will be to debug.

Remember, however, that this must be a balance with coupling. A group of extremely cohesive modules might also be tightly coupled together. We are looking for the balance between the two. If you remember one thing from these sections, it's that we want "loose coupling and strong cohesion".

Implementation & Deployment🔽

Good design allows us to see our entire project before it is built. With this, we can decide which areas we want to build and which areas we want to purchase. The great thing about purchasing code is that it is almost always cheaper.

It's almost always a win-win situation to purchase instead of build. Coding, however, is usually very specific. This makes it rare to find software that perfectly fits the problem. However, do some research before you begin building; you can save a lot of money.

🟢 Take Care of Programmers

Implementation is where most time is spent and where a lot of time can be lost.
Always program while alert and focused.
35 hours of programming can be just as productive as 70.
Programming takes focus; constant interruptions will reduce overall focus.

🟢 Coding Principles

Use a style guide so all the code looks relatively the same.
Code is written for people, not for computers.
Make modules easy to understand.
Go into everything with a plan.

🟢 Deployment Introduction

Deployment is a mix between testing and implementation; it mostly happens after testing but it requires implementations too.

The level of planning in this space is directly related to how the deployment affects the overall project.

Deployment should be built with the idea of retreat. If something goes wrong, how can we revert?

🟢 Deployment Planning

Amount of planning is dependent on the size of change.
We look at which areas which most likely have the biggest problems.
Areas to look at:
- Database
- Software Integration
- Runtime changes
- Training
- Downtimes
- Backups
- Network
- Memory
Finally, we need to look at planning steps to turn back.

🟢 Deployment Rollback

Rollback is the act of reverting a system back to the previous working state.

Look for a point of no return.
- This is a point where it takes longer to go back than it does to just continue through.
- Knowing this will help make a decision during deployment.
Every step of the deployment process makes a decision whether rollback is a better option.

Testing Overview🔽

Testing is the process of finding errors. These errors can either be failures within the code, or they can be failures to meet requirements. If the app doesn't do what it was set out to do, then that is a problem. In testing, we work to make sure the program works for all requirements.

Test Data: Inputs that are designed to test the system.
Test Cases: Ways in which we operate the system with the given data.
Oracle: The set of "good" outcomes.

🟢 Bugs

Bugs are, in essence, a deviation from expected behavior. For example, if you have a website, a potential bug might be that the website doesn't load. The code fatally breaks during loading and results in a lack of the service. This is, of course, a deviation from the expected behavior of being able to access the website.

Another bug could be if the website logged you into someone else's account by mistake. The website in this scenario is still up and functioning. However, a deviation from expected behavior, only being able to access your own account, has happened.

Failure: The event by which the code deviates from expected behavior.
Error: The part of the code that leads to the failure.
Fault: What the outcome actually was.

Testing can be used to show the presence of bugs but never to ensure the absence of them. This is because the only way to ensure this would be to test EVERY single possible test case. This could easily be in the billions to trillions for a typical system. This would be nearly impossible to do. The cost of testing alone could far exceed that of the entire rest of development times 10.

So what we do instead is try to do the smallest set of test cases to cover the greatest amount of ground. We use a mixture of different testing practices to accomplish this task. There will always be bugs, but we can actively remove most of them if we are smart.

🟢 Verification and Validation

Verification: Are we building the thing right? Does the software work compared to the given specifications.
Validation: Are we building the right thing? Does the software work compared to what the user/client needs?

A way of violating verification would be if the program accesses the wrong database. In this situation, we are not building the system correctly. It is deviating from expected behavior.

A way of violating validation would be if the program calculates car payments, instead of house payments. Our car payment calculator could be the most stable calculator in the world. However, we were supposed to build a house payment calculator. We are not building the right system. We are building a system that solves problems that it was not designed to solve. (And conversely doesn't solve problems that it was designed to solve.)

A more real-world example of this would be if a company has a really specific way of collecting information, and we designed a system that collects that information differently. All of the same information has been collected, but not in the right way. We aren't building the correct system.

Knowing and testing both of these are important to delivering high-quality software!

🟢 Testing

📌 Unit Testing

Unit testing to focus on the smallest unit of software. We are trying to isolate different areas, repeating until we test every module of the program. In this, we take a module, and we give it test cases. We then check these test cases against the oracle.

Overall, we are trying to make sure that the modules are doing what they were designed to do. We don't want to have bugs from other modules ruin the module we are testing. So for unit tests, we typically supply dummy values to isolate the module. This way, we know if a bug is happening, it's happening from the area we are testing.

📌 Integration Testing

Integration testing is the next step. Once we are satisfied that the modules are all working how we want them, we need to begin testing them together. With integration testing, we begin testing the architecture and the communication as a whole.

Where in unit testing we came up with test cases for individual modules, with integration testing, we will come up with test cases for groups of modules. For example, we might want to test an entire form, instead of just testing each box within the form.

📌 Incremental Testing

One of the issues with integration testing is that if a bug arises, it can be difficult to tell exactly what module introduced that bug. Incremental testing is one way of making this process easier.

With incremental testing, we slowly add one module after the next into the testing environment. This way, if a bug arises, we know which module caused the bugs to be introduced.

📌 Top-Down Testing

With Top-Down Testing, we begin testing at the highest possible level and then work our way down. To have this work, we need to have a set of dummy modules that we slowly replace with regular modules.

Stub: A template of the model that will be implemented. Typically returns hard-coded values.

A stub is typically used for this. It's a module that doesn't have any logic in it. All it has are functions that return hard-coded values. Hard-coded here means nothing that was calculated, just values that we put in there. So for example, a stub might have a function "getUser(int 45)". Instead of going through and finding information about a user, it just returns a user object that we have hard-coded into it.

This way, we can test our module's ability to use the user information without having to introduce another module. Once we test everything and make sure it works, we can then add in the actual module and do our next round of testing.

📌 Bottom-Up Testing

Bottom-Up Testing is the opposite of Top-Down. Here we work from the bottom and use things called drivers to make our way upwards.

Driver: Templates which execute commands and initialize the needed variables.

When we work from bottom up, we need the logic which calls our bottom modules. For example, if our module is the module with getUser() in it, then we need to figure out a way to properly test this. What we right is a driver, which initializes the variables and then makes multiple calls to the functions we need it to.

So in this situation, the driver might call getUser() 1,000,000 times with random values. We would then look at the results to see if the function is working the way we want it. Once we are satisfied, we add another layer and keep going.

📌 Back to Back Testing

Back to Back testing is good to do when you already have a working program. With this, we run a set of tests on the system before we make a change. We then make a change, by either updating, adding, or deleting a module. We then run the same set of tests.

We now have two sets of data. The before and the after. We compare these two sets to make sure that there are no differences. If there is a difference, we know the change did more than we like. We can then revert or go fix the bug.

📌 Manual vs Automatic Testing

There are two different ways we can set up tests. We can do it manually, where we enter values ourselves, or automatically, where we set up another system to do the test.

Automatic

Both of these have their own merits. Automatic testing is nice because we can cover a much larger area than manual testing. We can set up a system and have it test millions of test cases. Once we set it up, we can run it every time we change our code, guaranteeing we didn't break anything with a new update.

This all, however, comes with a lot more overhead. The testing system is a system itself. This means more planning and development time. On top of this, we will have to design tests which can be done from the computer and provide oracles to check their data. If we are testing out millions of test cases, the oracle will most likely have to be a complex algorithm.

There also comes the issue of testing the tester. What if our tester is designed incorrectly and it gives us false positives or negatives? We then spend unnecessary time chasing down phantom bugs or deploy bad software.

Manual

Manual testing involves a human being testing the code. They user goes in expecting to do an action. They then test to see if there is expected behavior. Bugs are noted down, and then given to developers.

These testers can be developers, stakeholders, or specially trained testers. They can more easily come up with oracles and perform a wide range of tests.

However, humans are very slow when compared to a computer. They can only test so much of a system. Often times, this means many parts of the system don't get properly tested.

Overall

The best way to test is to combine these two. Have a good set of automatic tests and a good set of testers. This will allow the speed of automatic, and the ease and flexibility of manual.

📌 Black Box vs White Box Testing

Depending on who tests can also have a large difference on testing. In the testing world, there is this idea of "white box" and "black box" testing.

With black box testing, we are testing the device based on what it is supposed to do. This is what most users will do when they get the software. They will try to perform all the actions they want. Test all of the expected behavior and make sure the program is working for them. If a bug arises, it will typically be because of a failure to meet the requirements of the program.

With white box testing, we are testing the system on the inside. This is where our automatic testing comes in. We test all different angles and aspects of the program to make sure it works. Here we are looking to test different combinations of different situations. For example, with a program that calculates house payments, we will want to test multiple interest rates, home prices, etc.

This is the technical side of things. This allows us to test all different sides of the system.

Software Maintenance🔽

Software Maintenance is the process of modifying the software after it has been delivered. In essence, it's the time after the honeymoon. This is the longest and most expensive part of the life of the software.

The majority of maintenance is usually spent fixing bugs.

🟢 Types of Maintenance

There are three types of maintenance:

📌 Corrective Maintenance

Corrective maintenance is where we go in and fix bugs. We fix the code, so it does what it was supposed to do in the first place. This type of maintenance is a reaction to the unexpected issues that arise during use. It typically consumes a large portion of maintenance budgets.

📌 Adaptive Maintenance

Adaptive maintenance is where we change the software to handle a change in the environment. This is when something in the environment changes, and we need to change our software to accommodate that.

For example, let's say we are building an application that uses the Facebook API. Facebook changes their API. Now our software is no longer functioning correctly because Facebook changed. Adaptive maintenance is the process of going into our software and making sure it works again.

📌 Perfective Maintenance

Perfective maintenance is where we go in and add functionality to the system. We don't fix anything, and we don't change the system to handle a new environment. Instead, we go in and make the system better.

For example, we have a program that calculates house payments. We might add functionality that lets us calculate car payments as well. This is now additional functionality that we have added. This is now what we will be doing the majority of the time. A well-crafted program should be able to be easily maintained and expanded in the future.

🟢 Version Control Systems

Version control is the means by which we track changes in our codebase over time. It's a record of all the changes we have made and is stored in a way to allow us to see exactly how we got to where we are today.

📌 Why Do We Use It?

Version control is important because it allows us to do a few things:

Revert to Old Versions: If we ever make a mistake, we can revert back to an old version of the code.
Branching: We can split off from the main version of the code to add a feature or try something new.
Collaboration: Multiple people can be working on the same code at the same time.
History: Every change is recorded, along with who made the change and why.

📌 Basic Concepts

Repository: This is the record of all of the different versions of the code.
Commit: This is when a user decides that they want to record the changes that have been made to the code. This is like a save point in a game.
Branch: A separate path of the code that isn't the main version.
Merge: When you take the changes that you have made in a separate branch and add them to the main branch.
Pull Request: This is when you have made a branch, made changes, and then asked for those changes to be merged into the main version of the code.

🟢 Conclusion

We've covered a lot of ground in this guide, from the fundamentals of software engineering to various architecture patterns, design principles, and the importance of modularity, coupling, cohesion, implementation, deployment, testing, and software maintenance. Additionally, we explored version control systems as a crucial tool for managing code changes and collaboration.

Remember, software engineering is an ever-evolving field, and staying curious, open to learning, and adaptable will serve you well in navigating the dynamic landscape of technology. Whether you are a seasoned developer or just starting on your software engineering journey, continue to explore, experiment, and build as you contribute to the exciting world of software development.