💡 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗻-𝗱𝗲𝗺𝗮𝗻𝗱 𝘀𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲!
Start learning ML for FREE and boost your resume with a certification 🏆
📊 Hands-on learning
🎓 Certificate included
🚀 Career-ready skills
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
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👉 Don’t miss this opportunity
Start learning ML for FREE and boost your resume with a certification 🏆
📊 Hands-on learning
🎓 Certificate included
🚀 Career-ready skills
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
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👉 Don’t miss this opportunity
🚀 Roadmap to Become a Software Architect 👨💻
📂 Programming & Development Fundamentals
∟📂 Master One or More Programming Languages (Java, C#, Python, etc.)
∟📂 Learn Data Structures & Algorithms
∟📂 Understand Design Patterns & Best Practices
📂 Software Design & Architecture Principles
∟📂 Learn SOLID Principles & Clean Code Practices
∟📂 Master Object-Oriented & Functional Design
∟📂 Understand Domain-Driven Design (DDD)
📂 System Design & Scalability
∟📂 Learn Microservices & Monolithic Architectures
∟📂 Understand Load Balancing, Caching & CDNs
∟📂 Dive into CAP Theorem & Event-Driven Architecture
📂 Databases & Storage Solutions
∟📂 Master SQL & NoSQL Databases
∟📂 Learn Database Scaling & Sharding Strategies
∟📂 Understand Data Warehousing & ETL Processes
📂 Cloud Computing & DevOps
∟📂 Learn Cloud Platforms (AWS, Azure, GCP)
∟📂 Understand CI/CD & Infrastructure as Code (IaC)
∟📂 Work with Containers & Kubernetes
📂 Security & Performance Optimization
∟📂 Master Secure Coding Practices
∟📂 Learn Authentication & Authorization (OAuth, JWT)
∟📂 Optimize System Performance & Reliability
📂 Project Management & Communication
∟📂 Work with Agile & Scrum Methodologies
∟📂 Collaborate with Cross-Functional Teams
∟📂 Improve Technical Documentation & Decision-Making
📂 Real-World Experience & Leadership
∟📂 Design & Build Scalable Software Systems
∟📂 Contribute to Open-Source & Architectural Discussions
∟📂 Mentor Developers & Lead Engineering Teams
📂 Interview Preparation & Career Growth
∟📂 Solve System Design Challenges
∟📂 Master Architectural Case Studies
∟📂 Network & Apply for Software Architect Roles
✅ Get Hired as a Software Architect
React "❤️" for More 👨💻
📂 Programming & Development Fundamentals
∟📂 Master One or More Programming Languages (Java, C#, Python, etc.)
∟📂 Learn Data Structures & Algorithms
∟📂 Understand Design Patterns & Best Practices
📂 Software Design & Architecture Principles
∟📂 Learn SOLID Principles & Clean Code Practices
∟📂 Master Object-Oriented & Functional Design
∟📂 Understand Domain-Driven Design (DDD)
📂 System Design & Scalability
∟📂 Learn Microservices & Monolithic Architectures
∟📂 Understand Load Balancing, Caching & CDNs
∟📂 Dive into CAP Theorem & Event-Driven Architecture
📂 Databases & Storage Solutions
∟📂 Master SQL & NoSQL Databases
∟📂 Learn Database Scaling & Sharding Strategies
∟📂 Understand Data Warehousing & ETL Processes
📂 Cloud Computing & DevOps
∟📂 Learn Cloud Platforms (AWS, Azure, GCP)
∟📂 Understand CI/CD & Infrastructure as Code (IaC)
∟📂 Work with Containers & Kubernetes
📂 Security & Performance Optimization
∟📂 Master Secure Coding Practices
∟📂 Learn Authentication & Authorization (OAuth, JWT)
∟📂 Optimize System Performance & Reliability
📂 Project Management & Communication
∟📂 Work with Agile & Scrum Methodologies
∟📂 Collaborate with Cross-Functional Teams
∟📂 Improve Technical Documentation & Decision-Making
📂 Real-World Experience & Leadership
∟📂 Design & Build Scalable Software Systems
∟📂 Contribute to Open-Source & Architectural Discussions
∟📂 Mentor Developers & Lead Engineering Teams
📂 Interview Preparation & Career Growth
∟📂 Solve System Design Challenges
∟📂 Master Architectural Case Studies
∟📂 Network & Apply for Software Architect Roles
✅ Get Hired as a Software Architect
React "❤️" for More 👨💻
❤5
✅ Top Web Development Interview Questions & Answers 🌐💻
📍 1. What is the difference between Frontend and Backend development?
Answer: Frontend deals with the part of the website users interact with (UI/UX), using HTML, CSS, JavaScript frameworks like React or Vue. Backend handles server-side logic, databases, and APIs using languages like Node.js, Python, or PHP.
📍 2. What is REST and why is it important?
Answer: REST (Representational State Transfer) is an architectural style for designing APIs. It uses HTTP methods (GET, POST, PUT, DELETE) to manipulate resources and enables communication between client and server efficiently.
📍 3. Explain the concept of Responsive Design.
Answer: Responsive Design ensures web pages render well on various devices and screen sizes by using flexible grids, images, and CSS media queries.
📍 4. What are CSS Flexbox and Grid?
Answer: Both are CSS layout modules. Flexbox is for one-dimensional layouts (row or column), while Grid manages two-dimensional layouts (rows and columns), simplifying complex page structures.
📍 5. What is the Virtual DOM in React?
Answer: A lightweight copy of the real DOM that React uses to efficiently update only parts of the UI that changed, improving performance.
📍 6. How do you handle authentication in web applications?
Answer: Common methods include sessions with cookies, tokens like JWT, OAuth, or third-party providers (Google, Facebook).
📍 7. What is CORS and how do you handle it?
Answer: Cross-Origin Resource Sharing (CORS) is a security feature blocking requests from different origins. Handled by setting appropriate headers on the server to allow trusted domains.
📍 8. Explain Event Loop and Asynchronous programming in JavaScript.
Answer: Event Loop allows JavaScript to perform non-blocking actions by handling callbacks, promises, and async/await, enabling concurrency even though JS is single-threaded.
📍 9. What is the difference between SQL and NoSQL databases?
Answer: SQL databases are relational, use structured schemas with tables (e.g., MySQL). NoSQL databases are non-relational, schema-flexible, and handle unstructured data (e.g., MongoDB).
📍 🔟 What are WebSockets?
Answer: WebSockets provide full-duplex communication channels over a single TCP connection, enabling real-time data flow between client and server.
💡 Pro Tip: Back answers with examples or a small snippet, and relate them to projects you’ve built. Be ready to explain trade-offs between technologies.
❤️ Tap for more!
📍 1. What is the difference between Frontend and Backend development?
Answer: Frontend deals with the part of the website users interact with (UI/UX), using HTML, CSS, JavaScript frameworks like React or Vue. Backend handles server-side logic, databases, and APIs using languages like Node.js, Python, or PHP.
📍 2. What is REST and why is it important?
Answer: REST (Representational State Transfer) is an architectural style for designing APIs. It uses HTTP methods (GET, POST, PUT, DELETE) to manipulate resources and enables communication between client and server efficiently.
📍 3. Explain the concept of Responsive Design.
Answer: Responsive Design ensures web pages render well on various devices and screen sizes by using flexible grids, images, and CSS media queries.
📍 4. What are CSS Flexbox and Grid?
Answer: Both are CSS layout modules. Flexbox is for one-dimensional layouts (row or column), while Grid manages two-dimensional layouts (rows and columns), simplifying complex page structures.
📍 5. What is the Virtual DOM in React?
Answer: A lightweight copy of the real DOM that React uses to efficiently update only parts of the UI that changed, improving performance.
📍 6. How do you handle authentication in web applications?
Answer: Common methods include sessions with cookies, tokens like JWT, OAuth, or third-party providers (Google, Facebook).
📍 7. What is CORS and how do you handle it?
Answer: Cross-Origin Resource Sharing (CORS) is a security feature blocking requests from different origins. Handled by setting appropriate headers on the server to allow trusted domains.
📍 8. Explain Event Loop and Asynchronous programming in JavaScript.
Answer: Event Loop allows JavaScript to perform non-blocking actions by handling callbacks, promises, and async/await, enabling concurrency even though JS is single-threaded.
📍 9. What is the difference between SQL and NoSQL databases?
Answer: SQL databases are relational, use structured schemas with tables (e.g., MySQL). NoSQL databases are non-relational, schema-flexible, and handle unstructured data (e.g., MongoDB).
📍 🔟 What are WebSockets?
Answer: WebSockets provide full-duplex communication channels over a single TCP connection, enabling real-time data flow between client and server.
💡 Pro Tip: Back answers with examples or a small snippet, and relate them to projects you’ve built. Be ready to explain trade-offs between technologies.
❤️ Tap for more!
❤3
𝗙𝗥𝗘𝗘 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗮𝗿𝗻𝗶𝘃𝗮𝗹 𝗯𝘆 𝗛𝗖𝗟 𝗚𝗨𝗩𝗜😍
Prove your skills in an online hackathon, clear tech interviews, and get hired faster
Highlightes:-
- 21+ Hiring Companies & 100+ Open Positions to Grab
- Get hired for roles in AI, Full Stack, & more
Experience the biggest online job fair with Career Carnival by HCL GUVI
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
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𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
❤9
This repository collects everything you need to use AI and LLM in your projects.
120+ libraries, organized by development stages:
→ Model training, fine-tuning, and evaluation
→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security
Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
120+ libraries, organized by development stages:
→ Model training, fine-tuning, and evaluation
→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security
Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
❤6
𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗚𝗲𝘁 𝗛𝗶𝗴𝗵 𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯 𝗜𝗻 𝟮𝟬𝟮𝟲😍
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❤3
Java vs Python Programming: Quick Comparison ✍
📌 Java Programming
• Strongly typed language
• Object-oriented
• Compiled, runs on JVM
Best fields:
• Backend development
• Enterprise systems
• Android development
• Large-scale applications
Job noscripts:
• Java Developer
• Backend Engineer
• Software Engineer
• Android Developer
Hiring reality:
• Popular in MNCs and legacy systems
• Used in banking and enterprise apps
India salary range:
• Fresher: 4–7 LPA
• Mid-level: 8–18 LPA
Real tasks:
• Build REST APIs
• Backend services
• Android apps
• Large transaction systems
📌 Python Programming
• Dynamically typed
• Simple syntax
• Interpreted language
Best fields:
• Data Analytics
• Data Science
• Machine Learning
• Automation
• Backend development
Job noscripts:
• Python Developer
• Data Analyst
• Data Scientist
• ML Engineer
Hiring reality:
• High demand in startups and AI teams
• Preferred for rapid development
India salary range:
• Fresher: 6–10 LPA
• Mid-level: 12–25 LPA
Real tasks:
• Data analysis noscripts
• ML models
• Automation tools
• APIs with Django or FastAPI
⚔️ Quick comparison
• Data handling: Java focuses on structured systems, Python handles data and files easily
• Speed: Java runs faster in production, Python runs slower but builds faster
• Learning: Java has steep learning curve, Python is beginner-friendly
🎯 Role-based choice
• Backend Developer: Java for scalability, Python for quick APIs
• Data Analyst: Python preferred, Java rarely used
• Data Scientist: Python mandatory, Java optional
• Android Developer: Java required, Python not used
✅ Best career move
• Start with Python for quick entry
• Add Java for strong backend roles
• Pick based on your target job
Which one do you prefer?
Java 👍
Python ❤️
Both 🙏
None 😮
📌 Java Programming
• Strongly typed language
• Object-oriented
• Compiled, runs on JVM
Best fields:
• Backend development
• Enterprise systems
• Android development
• Large-scale applications
Job noscripts:
• Java Developer
• Backend Engineer
• Software Engineer
• Android Developer
Hiring reality:
• Popular in MNCs and legacy systems
• Used in banking and enterprise apps
India salary range:
• Fresher: 4–7 LPA
• Mid-level: 8–18 LPA
Real tasks:
• Build REST APIs
• Backend services
• Android apps
• Large transaction systems
📌 Python Programming
• Dynamically typed
• Simple syntax
• Interpreted language
Best fields:
• Data Analytics
• Data Science
• Machine Learning
• Automation
• Backend development
Job noscripts:
• Python Developer
• Data Analyst
• Data Scientist
• ML Engineer
Hiring reality:
• High demand in startups and AI teams
• Preferred for rapid development
India salary range:
• Fresher: 6–10 LPA
• Mid-level: 12–25 LPA
Real tasks:
• Data analysis noscripts
• ML models
• Automation tools
• APIs with Django or FastAPI
⚔️ Quick comparison
• Data handling: Java focuses on structured systems, Python handles data and files easily
• Speed: Java runs faster in production, Python runs slower but builds faster
• Learning: Java has steep learning curve, Python is beginner-friendly
🎯 Role-based choice
• Backend Developer: Java for scalability, Python for quick APIs
• Data Analyst: Python preferred, Java rarely used
• Data Scientist: Python mandatory, Java optional
• Android Developer: Java required, Python not used
✅ Best career move
• Start with Python for quick entry
• Add Java for strong backend roles
• Pick based on your target job
Which one do you prefer?
Java 👍
Python ❤️
Both 🙏
None 😮
❤8👍2
𝗧𝗼𝗽 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗢𝗳𝗳𝗲𝗿𝗲𝗱 𝗕𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲 & 𝗜𝗜𝗠 𝗠𝘂𝗺𝗯𝗮𝗶😍
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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗜 :- https://pdlink.in/49UZfkX
𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:- https://pdlink.in/4pYWCEK
𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4tcUPia
Hurry..Up Only Limited Seats Available
❤3
Few common problems with lot of resumes:
1. 𝐈𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧.
I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones.
2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬.
Many resumes have the same common projects, such as:
Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps).
Most common projects are: -
Tic-tac-toe game.
Sorting algorithms visualizers.
To-do application.
Movie listing.
The codes for these projects are often copied and pasted from GitHub repositories.
Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview.
I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects.
Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands.
I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this.
3. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚.
For technical roles, adding quantitative data has a big impact.
For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."
1. 𝐈𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧.
I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones.
2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬.
Many resumes have the same common projects, such as:
Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps).
Most common projects are: -
Tic-tac-toe game.
Sorting algorithms visualizers.
To-do application.
Movie listing.
The codes for these projects are often copied and pasted from GitHub repositories.
Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview.
I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects.
Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands.
I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this.
3. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚.
For technical roles, adding quantitative data has a big impact.
For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."
❤7
🧩 Core Computer Science Concepts
🧠 Big-O Notation
🗂️ Data Structures
🔁 Recursion
🧵 Concurrency vs Parallelism
📦 Memory Management
🔒 Race Conditions
🌐 Networking Basics
⚙️ Operating Systems
🧪 Testing Strategies
📐 System Design
React ❤️ for more like this
🧠 Big-O Notation
🗂️ Data Structures
🔁 Recursion
🧵 Concurrency vs Parallelism
📦 Memory Management
🔒 Race Conditions
🌐 Networking Basics
⚙️ Operating Systems
🧪 Testing Strategies
📐 System Design
React ❤️ for more like this
❤8🥰1
𝗜𝗻𝗱𝗶𝗮’𝘀 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 | 𝗔𝗜 𝗜𝗺𝗽𝗮𝗰𝘁 𝗕𝘂𝗶𝗹𝗱𝗮𝘁𝗵𝗼𝗻😍
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
a flagship initiative of the Government of India 🇮🇳
Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
a flagship initiative of the Government of India 🇮🇳
❤1