Jay @jayprogrammer07

DSA isn’t about solving 3,500 problems it’s about recognizing the right pattern at the right time. Master patterns first, and speed + confidence follow automatically. 🙌💯 Here are the Top 15 DSA Patterns you need to know. 1. Two Pointers Use: Use two pointers to traverse a structure or range. Examples: 3Sum, Container With Most Water 2. Sliding Window Use : Maintain a window of elements and slide it to find solution. Examples: Longest Substring Without Repeating Characters 3. Fast & Slow Pointers Use two pointers at different speeds. Examples: Linked List Cycle, Middle of Linked List 4. Binary Search Use : Divide search space in half to find the answer. Examples: Search in Rotated Sorted Array 5. Binary Search on Answer Use: Binary search the possible answer space. Examples: Koko Eating Bananas, Minimum Days to Make Bouquets 6. Merge Intervals Use : Merge overlapping intervals to simplify. Examples: Merge Intervals, Insert Interval 7. Backtracking Use : Try all possible choices and backtrack. Examples: N-Queens, Sudoku Solver 8. DFS (Depth First Search) Use : Go deep in one path before backtracking. Examples: Number of Islands, Clone Graph 9. BFS (Breadth First Search) Use : Explore level by level using a queue. Examples: Shortest Path in Binary Matrix 10. Topological Sort Use : Linear ordering of tasks with dependencies. Examples: Course Schedule, Alien Dictionary 11. Dynamic Programming Use : Solve by breaking into subproblems and storing results. Examples: Climbing Stairs, Coin Change 12. Greedy Use : Make the best local choice at each step. Examples: Dijkstra, Union Find, Graph Coloring 13. Graph Use : Work with nodes and edges. Examples: Implement Trie, Word Search II 14. Tries (Prefix Tree) Use : Tree-based structure for strings. Examples: Implement Trie, Word Search II 15. Heap / Priority Queue Use: Use heap to efficiently get min/max. Examples: Kth Largest Element, Merge K Sorted Lists Kth Largest Element → Use a min-heap of size `k` to find the kth largest element in an array. Do follow AdarshChetan for more such amazing stuff ♥️ #career #jobs #opportunity #jobupdates #hiring #SDE #remote #freshers

12 Git commands you should know

🧵 Day 29/30 — #SystemDesign Sending a message feels instant. But behind that simple “Send” button, real-time chat systems handle: → Millions of concurrent connections → Message delivery guarantees → Online/offline users → Notifications → Read receipts → Media uploads → Synchronization across devices Building chat at scale is far more complex than it looks. Modern chat systems usually rely on WebSockets for persistent real-time communication. Instead of repeatedly asking the server for updates, the client maintains an open connection: User A ⇄ Chat Server ⇄ User B This allows messages to be pushed instantly. But real systems need much more than message transfer. Core components often include: → WebSocket Gateway → Message Queue (Kafka/RabbitMQ) → Presence Service (“online/offline”) → Notification Service → Media Storage → Database for chat history → Distributed cache (Redis) Large systems separate these responsibilities into multiple services. What happens if User B is offline? The system: → Stores message in DB → Pushes mobile notification → Syncs unread messages later → Updates delivery status when user reconnects This is why chat systems require strong synchronization logic. Platforms like: → WhatsApp → Discord → Slack → Telegram → Messenger invest heavily in low-latency infrastructure because users notice delays instantly in communication products. In chat systems, speed is not a feature. It’s the product itself. #30DaysOfSystemDesign #ChatArchitecture #BackendEngineering

Nitin.nn @NitinthisSide_

2 months ago

🧵 Day 28/30 — #SystemDesign Deploying code directly to all users sounds risky… because it is. Imagine releasing a new feature and suddenly: → Payments fail → API latency spikes → Mobile app crashes → Entire production breaks That’s why modern systems use Feature Flags.

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🧵 Day 28/30 — #SystemDesign Deploying code directly to all users sounds risky… because it is. Imagine releasing a new feature and suddenly: → Payments fail → API latency spikes → Mobile app crashes → Entire production breaks That’s why modern systems use Feature Flags. Feature flags allow companies to deploy code to production without immediately enabling it for everyone. The feature stays hidden behind a controllable switch. This means teams can: → Gradually roll out features → Test with small user groups → Run A/B experiments → Instantly disable buggy features → Reduce deployment risk Deployment and release become separate decisions. Example: Instagram wants to test a new UI. Instead of releasing globally: → Enable for 1% users → Monitor metrics → Increase to 10% → Roll out safely If something breaks: → Turn OFF flag → No rollback needed That’s extremely powerful in production systems. Feature flags are heavily used by: → Netflix → Google → Facebook → Uber → Amazon Because modern engineering is not just about shipping fast. It’s about shipping safely. #30DaysOfSystemDesign #FeatureFlags #BackendEngineering

Nitin.nn @NitinthisSide_

2 months ago

🧵 Day 27/30 — #SystemDesign Your system is running in production. Users report slow APIs. Payments randomly fail. CPU looks normal. Logs are huge. Nobody knows where the actual problem is. This is where Observability becomes critical. Observability is the ability to

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JVM Internals Cheatsheet

7 Must-Know Big-O Complexities for Coding Interviews: 1. 𝐎(1) - 𝐂𝐨𝐧𝐬𝐭𝐚𝐧𝐭 𝐭𝐢𝐦𝐞 - The runtime doesn't change regardless of the input size. - Example: Accessing an element in an array by its index. 2. 𝐎(𝐥𝐨𝐠 𝐧) - 𝐋𝐨𝐠𝐚𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐭𝐢𝐦𝐞 - The runtime grows slowly as the input size increases. Typically seen in algorithms that divide the problem in half with each step. - Example: Binary search in a sorted array. 3. 𝐎(𝐧) - 𝐋𝐢𝐧𝐞𝐚𝐫 𝐭𝐢𝐦𝐞 - The runtime grows linearly with the input size. - Example: Finding an element in an array by iterating through each element. 4. 𝐎(𝐧 𝐥𝐨𝐠 𝐧) - 𝐋𝐢𝐧𝐞𝐚𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐭𝐢𝐦𝐞 - The runtime grows slightly faster than linear time. It involves a logarithmic number of operations for each element in the input. - Example: Sorting an array using quick sort or merge sort. 5. 𝐎(𝐧^2) - 𝐐𝐮𝐚𝐝𝐫𝐚𝐭𝐢𝐜 𝐭𝐢𝐦𝐞 - The runtime grows proportionally to the square of the input size. - Example: Bubble sort algorithm which compares and potentially swaps every pair of elements. 6. 𝐎(2^𝐧) - 𝐄𝐱𝐩𝐨𝐧𝐞𝐧𝐭𝐢𝐚𝐥 𝐭𝐢𝐦𝐞 - The runtime doubles with each addition to the input. These algorithms become impractical for larger input sizes. - Example: Generating all subsets of a set. 7. 𝐎(𝐧!) - 𝐅𝐚𝐜𝐭𝐨𝐫𝐢𝐚𝐥 𝐭𝐢𝐦𝐞 - Runtime is proportional to the factorial of the input size. - Example: Generating all permutations of a set. ♻️ Repost to help others learn this

🧵 Day 27/30 — #SystemDesign Your system is running in production. Users report slow APIs. Payments randomly fail. CPU looks normal. Logs are huge. Nobody knows where the actual problem is. This is where Observability becomes critical. Observability is the ability to understand what’s happening inside a system by analyzing its outputs — mainly: → Logs → Metrics → Traces Without observability, debugging distributed systems becomes guesswork. ⸻ Logs tell you what happened. Example: → Error messages → Request details → Stack traces → Authentication failures Useful for deep debugging. ⸻ Metrics tell you how the system is behaving. Example: → CPU usage → API latency → Request count → Error rates → Memory consumption Useful for monitoring health and detecting anomalies. ⸻ Traces tell you where time is spent across services. In microservices, one request may travel through: API Gateway → Auth Service → Payment Service → Database Distributed tracing helps visualize the full journey and identify bottlenecks. ⸻ Modern production systems use observability stacks like: → Prometheus + Grafana → ELK Stack → OpenTelemetry → Jaeger → Datadog → New Relic Companies like Uber, Netflix, Google, and Amazon heavily invest in observability because scaling systems is impossible if engineers cannot see failures clearly. Monitoring tells you something is wrong. Observability helps you understand why. #30DaysOfSystemDesign #Observability #BackendEngineering

Nitin.nn @NitinthisSide_

2 months ago

🧵 Day 26/30 — #SystemDesign Retries seem harmless. An API fails → retry the request. Still fails → retry again. Simple… until thousands of servers start retrying together and accidentally take the entire system down. That’s why production systems use Retry Strategies with

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🧵 Day 26/30 — #SystemDesign Retries seem harmless. An API fails → retry the request. Still fails → retry again. Simple… until thousands of servers start retrying together and accidentally take the entire system down. That’s why production systems use Retry Strategies with Exponential Backoff instead of blind retries. A retry mechanism helps recover from temporary failures like: → Network instability → Timeout issues → Short server overloads → Rate limiting But retrying instantly creates traffic spikes during failures. Exponential backoff solves this by increasing delay after every failed attempt. Example: → Retry 1 → wait 1s → Retry 2 → wait 2s → Retry 3 → wait 4s → Retry 4 → wait 8s This gives systems time to recover instead of getting overwhelmed. Modern systems also add Jitter (randomness in delay) so millions of clients don’t retry at the exact same moment. Without jitter: → Retry storm → Traffic spikes → Cascading failures With jitter: → Requests spread naturally → Better recovery behavior → More stable systems That’s why companies like AWS, Google, Stripe, and Netflix heavily recommend exponential backoff patterns in distributed systems. Retries improve resilience. Uncontrolled retries destroy resilience. #30DaysOfSystemDesign #DistributedSystems #BackendEngineering

Nitin.nn @NitinthisSide_

2 months ago

🧵 Day 25/30 — #SystemDesign Authentication and Authorization sound similar, but they solve completely different problems in backend systems. Authentication answers: “Who are you?” The system verifies identity using passwords, OTPs, sessions, JWTs, OAuth, biometrics, etc.

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How traceroute works?

🧵 Day 25/30 — #SystemDesign Authentication and Authorization sound similar, but they solve completely different problems in backend systems. Authentication answers: “Who are you?” The system verifies identity using passwords, OTPs, sessions, JWTs, OAuth, biometrics, etc. Authorization answers: “What are you allowed to do?” After login, the system checks permissions, roles, and access levels before allowing actions like deleting users, accessing admin routes, viewing private data, or triggering payments. ⸻ A user can be authenticated but still not authorized. Example: You log into Netflix successfully → Authentication ✅ Trying to access Netflix admin dashboard → Authorization ❌ This distinction becomes critical in production systems because bad authorization design can expose sensitive data even when authentication is secure. Modern systems often use: → JWT / Sessions for authentication → RBAC (Role-Based Access Control) for authorization → OAuth for third-party identity access → Middleware/API Gateways for permission enforcement ⸻ Real companies implement authorization very deeply: → Google Docs controls document-level permissions → AWS IAM manages cloud access policies → GitHub controls repo/team permissions → Banking apps enforce strict action-based authorization Authentication gets users into the system. Authorization decides what power they actually have inside it. #30DaysOfSystemDesign #Authentication #BackendEngineering

Nitin.nn @NitinthisSide_

2 months ago

🧵 Day 24/30 — #SystemDesign REST vs GraphQL: How modern APIs deliver data differently Frontend apps need data fast.
But fetching too much or too little data creates performance problems. That’s why API design matters. Two popular approaches dominate modern backend systems: →

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@0xPrajwal_ HTML

Your husband clocks overtime he hates. Eats cold dinner because the kids come first Fixes the car in the rain. Zero complaints. Asks for closeness at bedtime → "I'm tired." I'm not in the mood After years of that, it makes sense if he looks elsewhere

AI agent–enabled coding is quietly becoming the new SDLC. Software development just had its biggest shift since the GUI. Planning. Coding. Testing. Deployment. Agents are starting to handle all of it. Here’s the shift most engineers haven’t noticed yet 👇 Old model: SDLC • sequential phases • human-driven execution • testing happens after development • changing requirements break timelines Everything moves step → by → step. New model: ADLC (Agent-Driven Lifecycle) • agents write, refactor, and test code • multiple tasks run in parallel • requirements evolve dynamically • feedback loops happen in real time Instead of a pipeline… You get a live development system. 6 major shifts happening right now 1️⃣ Driver Human execution → Autonomous agents 2️⃣ Planning Fixed scope → Evolving goals & PRDs 3️⃣ Development Speed Sequential handoffs → Parallel sub-agents 4️⃣ Testing Post-development QA → Continuous testing 5️⃣ Adaptability Mid-cycle chaos → Real-time re-planning 6️⃣ Feedback Loop End-of-project retros → Live monitoring Some early signals are already here. According to agentic coding reports: • teams at Wiz and CRED doubled execution speed • large-scale repos are being modified autonomously • complex implementations completed in hours instead of days How engineers should adapt 1️⃣ Start with one agent Automate testing first. 2️⃣ Learn to write clear PRDs Agents execute exactly what you define. 3️⃣ Introduce parallel sub-agents Break one large task into smaller workstreams. 4️⃣ Review outcomes, not every line of code 5️⃣ Build live feedback loops Agents should detect issues before you do. The future of software development isn’t just faster coding. It’s agent-driven systems building software. #AI #AIAgents #SoftwareEngineering #SDLC #GenAI #AIEngineering

This girl knows what's up.

SOLID Principles Explained with Clear Examples: 𝐒 - 𝐒𝐢𝐧𝐠𝐥𝐞 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 A class should have only one reason to change. - Example: Instead of one giant User class that handles authentication, profile updates, and sending emails, split it into UserAuth, UserProfile, and EmailService. 𝐎 - 𝐎𝐩𝐞𝐧/𝐂𝐥𝐨𝐬𝐞𝐝 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 Classes should be open for extension but closed for modification. - Example: Define a Shape interface with an area() method. When you need a new shape, just add a Circle or Triangle class that implements it. 𝐋 - 𝐋𝐢𝐬𝐤𝐨𝐯 𝐒𝐮𝐛𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 Subtypes must be substitutable for their base types without breaking behavior. - Example: If Bird has a fly() method, then Eagle and Sparrow should both work anywhere a Bird is expected. 𝐈 - 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 𝐒𝐞𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 Don't force classes to implement interfaces they don't use. - Example: Instead of one fat Machine interface with print(), scan(), and fax(), break it into Printable, Scannable, and Faxable. A SimplePrinter only implements Printable. 𝐃 - 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐲 𝐈𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 High-level modules should not depend on low-level modules. Both should depend on abstractions. - Example: Your OrderService should depend on a PaymentGateway interface, not directly on Stripe or PayPal. The real power of SOLID is not in following each principle in isolation. It's in how they work together to make your code easier to change, test, and extend. ♻️ Repost to help others in your network

> 29 year old woman > Watches feminist podcasts > Tells husband to do everything equally > Husband makes an expense list > Tells her to start paying for everything equally > Now she is crying All their feminism vanishes when asked to pay equal bills.

Same was done with me in every government institute. Even after scoring 95% in 10th and 12th, and securing good ranks in IIT and NEET, I was denied seats in top government colleges, saying “ye general category wale hain” to signal my caste. While those with even less than half my marks were given those seats..

Nitin Meshram @nitinmeshram_

5 months ago

In 2014, while I was negotiating to rent a house, the broker told the owner, ‘ye Ambedkar ji wale hai,’ to signal my caste. The rent was ₹45,000 plus ₹3,000 for maintenance, which I was ready to pay. Despite this, I was denied the house solely because of my caste. Caste

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This is the kind of content that matters most

I’m seeing a lot of people following me and engaging after this reply clearly, it struck a chord. I’m not into the followers game. But if you’re from the general category, believe in merit first, nation first, and talent over caste and if you’ve been a victim of this heinous caste-based system feel free to follow. Let our voices travel far and push India toward a future built on merit, science, and technology not discrimination. Jai Hind 🇮🇳🇮🇳

OAuth2 - All You Need to Know