Coding Interview Preparation

If you're prepping for system design interviews, this repo is gold

It contains a curated, systematic breakdown covering:
→ Interview tips (clarify scope → high-level design → component design)
→ CDN, cache, URL shortener, news feed, chat, search & more
→ Engineering blogs from top companies
→ OOD interview prep

🔗 Link

💼 Why Your Resume Gets Rejected Before a Human Reads It

You may have good skills and projects, but your resume can still get rejected in seconds. 👀

Here are some common reasons 👇

1. Too Much Information 📄
• A resume filled with everything you've ever done is hard to scan
• Keep only what's relevant to the role

2. Generic Projects 🛠
• "To-Do App" and "Calculator" don't show much by themselves
• Explain what you built, the technologies used and what problem you solved

3. No Measurable Results 📊
Instead of:

"Built a web application"

Try:

"Built a web application used to manage 500+ records with..."

Numbers make your work easier to understand.

4. Skill Dumping 🧠
• Listing 20 technologies doesn't prove you know them
• Be ready to explain anything you put on your resume

5. Poor Formatting 🚫
• Too many colors, graphics or complicated layouts
• Make your resume easy to scan and keep the important information obvious

Your resume isn't supposed to tell your entire story. It's supposed to make someone want to hear the rest.

🧠 Coding Myths You Should Stop Believing

There's a lot of advice online about learning to code.

But not all of it is true. 👀

Here are some common myths developers hear all the time 👇

❌ Myth #1: You Need to Know Mathematics to Code

✅ Reality: Most programming doesn't require advanced mathematics. Logic, problem-solving and practice matter much more for everyday development.

❌ Myth #2: You Need to Learn Many Programming Languages

✅ Reality: You can become a great developer by becoming really good at one language first.

❌ Myth #3: You Need 100+ LeetCode Problems

✅ Reality: Solving problems is useful, but understanding patterns and improving your problem-solving ability matters more than chasing a number.

❌ Myth #4: More GitHub Commits = Better Developer

✅ Reality: A developer with 20 meaningful commits can learn more than someone making hundreds of meaningless commits.

❌ Myth #5: AI Means You Don't Need to Learn Coding

✅ Reality: AI can write code, but you still need to understand what that code does, identify mistakes and make good technical decisions. 🤖

💡 Don't blindly follow coding advice. Understand the "why" behind it.

💾 Save this if you're learning to code.

💼 What Companies Actually Look For in a Fresher

Think companies only care about your CGPA or how many certificates you have? Not really. Here's what actually matters 👨‍💻🚀

1. Strong Fundamentals 🧠
• Understand programming, DSA and core CS concepts
• Don't just memorize answers

2. Problem-Solving Skills 🧩
• Learn how to break a problem into smaller parts
• Practice DSA and logical problems regularly

3. Real Projects 🛠
• Build projects that solve actual problems
• Be ready to explain your decisions and contributions

4. Good Understanding of Your Tech Stack ⚙️
• Know the tools and technologies you mention on your resume
• Avoid adding skills just because they're trending

5. Communication Skills 🗣
• Explain your approach clearly
• Good technical knowledge means little if you can't communicate it

6. Git & GitHub 🐙
• Understand version control and collaborative development
• Keep your projects organized and maintainable

7. Learning Ability 📚
• Technology keeps changing
• Companies value people who can learn new tools and adapt quickly

8. Internships & Experience 🚀
• Internships, hackathons and open-source contributions can strengthen your profile
• Focus on what you actually learned and built

9. Resume Quality 📄
• Keep it clear, relevant and easy to scan
• Highlight measurable achievements instead of filling it with buzzwords

💡 Certificates can help, but skills, projects and problem-solving are what you should focus on.

📌 Save this before preparing for placements.

💰 SALARY NEGOTIATION #5 - What to Do When They Say "This Is Our Final Offer"

This phrase is often a genuine limit - but sometimes it's also a negotiating tactic. Here's how to respond either way, without burning the relationship.

✅ If you still want more:

"I understand, and I really appreciate the transparency. If the base salary is fixed, is there any flexibility elsewhere - a signing bonus, additional equity, an accelerated first review, or extra PTO?"

This works because "final offer on base salary" doesn't necessarily mean "final offer on total compensation." Companies often have more flexibility on one-time bonuses or equity than they do on base salary bands (which are frequently constrained by internal leveling systems).

✅ If you decide to accept:

"Thank you, I'm happy to accept at this level. I'm excited to get started." - Simple, professional, no need to relitigate once you've decided.

⚠️ One thing to never do: threaten to walk away unless you're genuinely prepared to. An empty threat that gets called ("okay, we understand, good luck with your search") is far worse than just accepting gracefully or asking one more polite question.

Negotiation isn't a battle to "win" - it's a conversation to reach a number where both sides feel good moving forward. That mindset alone tends to produce better outcomes than an adversarial one.

Have you ever heard "final offer" and gotten more anyway by asking about non-salary levers? 👇

📚 Snowflake Interview Questions

Yesterday, One of our members requested for Snowflake Interview Questions.

System Design Concepts You Need to Master If I Wanted to Crush it.

1.Consistent Hashing
2.Sharding
3.CAP Theorem
4.Quorum Consensus
5.Leader Election
6.Raft & Paxos
7.Gossip Protocol
8.Vector Clocks
9.Load Shedding
10.Circuit Breakers
11.Backpressure
12.Tail Latency Reduction
13.Bloom Filters
14.HyperLogLog
15.Reservoir Sampling
16.Split-Brain Resolution

@coding_interview_preparation

🚫 5 Mistakes Beginners Make While Learning Coding

Learning to code is not just about writing more code. Avoiding these mistakes can save you months of frustration 👨‍💻⚡️

1. Learning Too Many Languages 🔄
• Jumping between Python, C++, Java and JavaScript
• Master one language before moving to another

2. Watching Tutorials Without Practicing 📺
• Tutorials feel productive, but passive learning isn't enough
• Write the code yourself and solve problems without copying

3. Trying to Learn Everything at Once 🧠
• DSA, Web Dev, AI, Cloud, Cybersecurity...
• Pick one direction and build a strong foundation first

4. Avoiding Projects 🛠
• Completing courses without building anything
• Projects help you turn concepts into real skills

5. Giving Up When You Get Stuck 😵‍💫
• Getting errors and not knowing the solution is normal
• Learn to debug, search documentation and understand the problem

💡 You don't need to know everything. You just need to keep improving.

📌 Save this if you're learning to code.

Sorting Algorithms Summary

🧠 DSA Topics You Should Learn in Order

Confused about what to learn in DSA? Follow this order and build your concepts step by step 👨‍💻🔥

🟢 Foundation

1. Time & Space Complexity ⏱️
• Understand Big O notation
• Learn how to analyze your solutions

2. Arrays & Strings 📦
• Master traversal, searching and basic manipulation
• Practice two pointers and sliding window

3. Recursion & Backtracking 🔄
• Understand recursive thinking
• Solve subsets, permutations and combination problems

🟡 Core Data Structures

4. Linked Lists 🔗
• Learn singly and doubly linked lists
• Practice reversal and cycle problems

5. Stacks & Queues 📚
• Understand LIFO and FIFO
• Learn monotonic stack and deque patterns

6. Hashing #️⃣
• Use hash maps and hash sets effectively
• Solve frequency and lookup-based problems

🔴 Advanced

7. Trees & Graphs 🌳
• Learn traversals, BFS and DFS
• Move towards harder graph problems

8. Heaps & Priority Queues ⛰
• Understand heap operations
• Practice top-K and scheduling problems

9. Dynamic Programming 🧩
• Start with 1D and 2D DP
• Gradually move to more complex patterns

10. Greedy & Advanced Algorithms ⚡️
• Learn greedy strategies, binary search and important algorithmic patterns

💡 Don't rush into advanced topics. Strong fundamentals make DSA much easier.

💾 Save this roadmap and follow it step by step.

@Coding_interview_preparation

📂 API Design Roadmap
┃
┣ 📂 Foundations
┃ ┣ 📂 What is an API?
┃ ┣ 📂 HTTP & HTTPS Fundamentals
┃ ┣ 📂 Request & Response Lifecycle
┃ ┣ 📂 JSON & Data Formats
┃ ┗ 📂 API Design Principles
┃
┣ 📂 REST API Design
┃ ┣ 📂 REST Architecture
┃ ┣ 📂 Resources & Endpoints
┃ ┣ 📂 HTTP Methods (GET, POST, PUT, DELETE, PATCH)
┃ ┣ 📂 Status Codes
┃ ┗ 📂 REST Best Practices
┃
┣ 📂 API Documentation
┃ ┣ 📂 OpenAPI Specification
┃ ┣ 📂 Swagger UI
┃ ┣ 📂 API Reference Documentation
┃ ┣ 📂 Examples & SDKs
┃ ┗ 📂 Versioned Documentation
┃
┣ 📂 Authentication & Authorization
┃ ┣ 📂 API Keys
┃ ┣ 📂 JWT Authentication
┃ ┣ 📂 OAuth 2.0 & OpenID Connect
┃ ┣ 📂 Role-Based Access Control (RBAC)
┃ ┗ 📂 Token Management
┃
┣ 📂 API Security
┃ ┣ 📂 HTTPS & TLS
┃ ┣ 📂 CORS Configuration
┃ ┣ 📂 CSRF & XSS Protection
┃ ┣ 📂 Rate Limiting & Throttling
┃ ┗ 📂 Input Validation & Sanitization
┃
┣ 📂 Advanced API Architectures
┃ ┣ 📂 GraphQL
┃ ┣ 📂 gRPC
┃ ┣ 📂 WebSockets
┃ ┣ 📂 Server-Sent Events (SSE)
┃ ┗ 📂 Event-Driven APIs
┃
┣ 📂 API Performance
┃ ┣ 📂 Pagination
┃ ┣ 📂 Filtering & Sorting
┃ ┣ 📂 Caching Strategies
┃ ┣ 📂 Compression
┃ ┗ 📂 Performance Optimization
┃
┣ 📂 API Reliability
┃ ┣ 📂 Error Handling
┃ ┣ 📂 Retry Strategies
┃ ┣ 📂 Idempotency
┃ ┣ 📂 Circuit Breaker Pattern
┃ ┗ 📂 Health Checks
┃
┣ 📂 API Testing
┃ ┣ 📂 Unit Testing
┃ ┣ 📂 Integration Testing
┃ ┣ 📂 Postman & Insomnia
┃ ┣ 📂 Load Testing
┃ ┗ 📂 Contract Testing
┃
┣ 📂 API Deployment
┃ ┣ 📂 API Gateways
┃ ┣ 📂 Reverse Proxies
┃ ┣ 📂 Docker & Containers
┃ ┣ 📂 CI/CD Pipelines
┃ ┗ 📂 Cloud Deployment
┃
┣ 📂 Monitoring & Observability
┃ ┣ 📂 Logging
┃ ┣ 📂 Metrics Collection
┃ ┣ 📂 Distributed Tracing
┃ ┣ 📂 Prometheus & Grafana
┃ ┗ 📂 API Analytics
┃
┣ 📂 AI-Powered APIs
┃ ┣ 📂 OpenAI API Integration
┃ ┣ 📂 Function Calling
┃ ┣ 📂 Streaming Responses
┃ ┣ 📂 AI Agent APIs
┃ ┗ 📂 Cost & Token Optimization
┃
┣ 📂 Real-World Projects
┃ ┣ 📂 Authentication API
┃ ┣ 📂 E-commerce REST API
┃ ┣ 📂 Payment Gateway API
┃ ┣ 📂 AI Chat API
┃ ┗ 📂 Microservices API Platform
┃
┣ 📂 Practice & Growth
┃ ┣ 📂 Build Public APIs
┃ ┣ 📂 Contribute to API Projects
┃ ┣ 📂 Write API Documentation
┃ ┣ 📂 API Design Reviews
┃ ┗ 📂 Interview Preparation
┃
┗ 📂 Career & Monetization
┣ 📂 Backend Engineer Roles
┣ 📂 API Platform Engineer
┣ 📂 SaaS Development
┣ 📂 API Consulting & Freelancing
┗ 📂 Continuous Learning

👉 Follow this consistently for 2–4 months and you'll be able to design, build, secure, and scale production-ready APIs with confidence.