Riya Kumar

Stripe pays ₹40–50 LPA for SDE-1 and ₹70–90 LPA for SDE-2 roles! 🤯 And here’s an interesting question they might ask: “How would you design a URL Shortener system like Bitly that scales to billions of requests per day?” Things to consider: ✅ Hashing vs Base62 encoding ✅ Collision handling and unique ID generation ✅ Read-heavy architecture and caching layers ✅ Expiry and analytics tracking But here’s the kicker - what if the same long URL is shortened multiple times by different users? Do you generate new short links every time, or return the same one? And how would you keep your service fast while preventing hash collisions across millions of URLs?

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🚨 Concurrency in production systems is tricky. Ever wondered how: Swiggy prevents two delivery agents from taking the same order? BookMyShow ensures only one person books seat A12? The answer: Redis — but not just as a cache. I explored two real-world patterns in Redis: 1️⃣ Slot Booking (Consistency-first) Distributed locks with atomic SETNX Only one user wins, others fail instantly 97% faster than traditional DB locks 2️⃣ Real-Time Order Matching (Speed-first) Pub/Sub broadcasts + atomic claim First agent gets the order, others stop immediately Sub-millisecond coordination 💡 Key insight: choose your pattern based on priority — consistency vs real-time responsiveness. I documented the full analysis with architecture flows, pitfalls, and production metrics here: https://lnkd.in/ga3zdBcF How do you handle concurrency in production? Share your patterns 👇 #Redis #SystemDesign #DistributedSystems #Backend #SoftwareEngineering

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As an engineer, I love talking about apps and architectures that quietly solve very real problems at scale. And here’s one that caught my eye recently. Most companies in India already run on UPI in practice, but their expense stack is still stuck in PDFs, Excel and ‘bhai, screenshot bhej do’. CUPI flips that: it lets teams keep paying via UPI, while every transaction is born structured, policy-aware and ready for finance from day zero. If you enjoy seeing messy real-world flows turned into clean rails, this is a fun one to explore: getcupi.com #EngineeringForBusiness #UPIStack #CUPIWorks

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We have generally worked with hashmap, but yes we do use Treemap when we handle specific conditions like if we want to store objects sorted, so let's see something differently. I faced this question in an interview (Wissen), is bit interesting and relatable as in how hashmap work, the question quoted: "How does treemap work internally and how does it sort objects (class instance) if stored as key" So, just keeping it noted!! What is TreeMap? A TreeMap is a key-value map that keeps keys sorted. Internally, it uses a self-balancing binary search tree, specifically a Red-Black Tree. Operations like put, get, remove take O(log n) time instead of average O(1) in hash‐based maps. 🧱 How TreeMap Stores Objects as Keys It stores references to key objects in tree nodes, along with their associated values. A node contains: the key object, the value object, references to left, right child nodes + parent, plus the color bit (red or black) for balancing. 📏 How Keys Are Ordered & Compared TreeMap requires a way to compare keys: either via the class implementing Comparable<T> or via a Comparator provided when you create the TreeMap. When you put(key, value), TreeMap uses compareTo or the comparator to decide where in the tree the new node should go: left if smaller, right if larger.(BST mechanism) ⚠️ What Happens If Two Keys “Compare Equal” If two distinct objects (different instances) compare as equal (i.e. compareTo(...) == 0), TreeMap treats them as the same key. The new value overwrites the old value. The old node is not duplicated. Example: If your Person class compares by age only, then new Person("Alice", 30) and new Person("Bob", 30) will map to the same key in TreeMap → inserting the second replaces the first. 🔧 Best Practice: Unique Comparison To avoid unintended overwriting, ensure your comparison logic includes tie-breaker fields. Example: Compare by age, and if ages are equal, compare by name. This way keys are unique as per your criteria. 🚫 “Collisions” in TreeMap vs HashMap In HashMap, “collision” usually means two keys hash to the same bucket; handled by chaining or treeification. In TreeMap, there are no hash collisions, since no hashing is used. The only “conflict” is when comparison returns zero → key is considered duplicate. **If you’re working with custom classes as TreeMap keys, always think: How will they compare? Is the comparison logic precise enough to distinguish instances I care about? #DataStructures #Java #TreeMap #Programming

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The number one reason candidates fail system design interviews isn't technical knowledge. It's time blindness. After years of conducting HLD and LLD rounds, here's a pattern I keep seeing: ✓ The candidate knows the concepts ✓ The design direction is solid ✓ They're clearly technically capable ...but the interview ends incomplete. They spend too much time explaining every thought in detail, without watching the clock. And suddenly, time's up. What gets lost: → Key tradeoffs never get discussed → No runway to recover from early mistakes → No time to optimize or iterate → The hiring signals I need stay hidden Time is part of the evaluation. Not because we want you to rush, but because real engineering is solving problems under constraints. A simple framework that helps: Requirements + estimation (first 5 mins) High-level design (next 10 mins) Deep dive + tradeoffs (bulk of the time) Buffer for iteration (last 10 mins) Even if your design isn't perfect, this ensures you cover what actually matters. My advice: → Talk in checkpoints, not monologues → Ask: "Should I go deeper here or move on?" → Leave buffer at the end — that's where strong engineers stand out Your architecture might be brilliant. But if I can't see it, I can't hire you for it. If you're preparing for system design interviews: practice with a timer, not just a whiteboard. #SystemDesign #TechInterviews #SoftwareEngineering #HLD #LLD

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The worst way to teach/interview/learn system design is to design the entire company (ex: Quora, Airbnb, PayPal, Paytm, Hotstar) 🤦‍♂️ When you pick such a big system, you will create hypothetical Microservices and will not have time to go into engineering depth. A good system design discussion is when a problem statement is focused. You get a chance to discuss the component in-depth and touch upon the engineering aspects like bottlenecks, locking, and transactions. Yahi to asli maza hai! Tackling engineering challenges at scale. This is why every single system I discuss in my course has a limited scope so that I can cover it in-depth from all directions. ps: arpitbhayani.me/course

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I have been taking interviews for backend engineers (5–12 yrs exp) for some time now. Just sharing few common patterns I keep noticing. 1. Too much focus on buzzwords Microservices, Kafka, Redis, System Design… all good. But when I go little deeper into basics (collections, threads, DB transactions), answers become shaky. Strong foundation still matters a lot. 2. Not able to explain their own project properly When I ask “Can you explain your current system architecture?” Many give very high-level answers. If you worked on something daily, you should be able to draw and explain it clearly. 3. No discussion about trade-offs At senior level, I expect thinking like: Why this approach? Why not something else? What are the pros/cons? There is no perfect design. Only better decisions for a given situation. 4. Random debugging approach If production issue comes, how will you approach? Some candidates jump directly to guessing. I look more for structured thinking than instant solution. 5. Communication at senior level After 8+ years, it’s not just coding. It’s ownership, clarity, mentoring ability and confidence in decision making. Just sharing observations. If you are preparing for interviews, focus on fundamentals + clarity of thought. Would love to hear what others have noticed.

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39 niche LLD questions to practice if you are aiming for Senior or Staff backend roles at top product companies in India. Senior engineers at Amazon, Microsoft, and Google in India can cross 75 LPA to 1 Cr. The interviews for those levels expect you to model systems & go beyond writing classes for a vending machine. So I took the most useful LLD problems and grouped 39 of them into patterns you actually see in work. [1] Resource and booking systems   Pattern: limited units, time slots, no double booking, fair allocation. ↳ Multi level parking management for cars and bikes in a mall ↳ Parking control that handles many gates and parallel entry or exit requests ↳ Controller that decides which lift should serve which floor request in a tall building ↳ Simple single lift controller for a small office ↳ Train platform assignment at a busy station where arrivals and departures overlap ↳ Meeting room calendar that prevents clashes between bookings ↳ Recurring meeting scheduler that handles series, edits to single instances, and cancellations ↳ City wide car rental flow from search to pickup, return, and billing [2] Money, commerce and pricing flows   Pattern: orders, balances, payments, refunds, leaderboards around money or points. ↳ Food ordering and delivery system for restaurants in a city ↳ Group expense tracker that lets friends split and settle bills ↳ Stock trading platform where users place market and limit orders ↳ Shopping cart for an online store with offers and stock checks ↳ Digital wallet for storing balance, sending money, and viewing history ↳ Order plus inventory tracking for a warehouse with concurrent updates ↳ Performance scoreboard for customer support agents based on daily ratings ↳ Fantasy sports leaderboard that ranks teams by total points across matches [3] Editors, spreadsheets and office tools   Pattern: document state, edits, formulas, undo, recalculation. ↳ Rich text editor for writing documents with headings, styles, and saving ↳ Editor that supports undo and redo of operations over a long history ↳ Lightweight editor that focuses on cursor movement and basic text commands ↳ Spreadsheet grid with rows and columns that stores values and formulas ↳ Formula engine that computes a SUM over ranges and updates when cells change [4] File systems, search and storage   Pattern: paths, trees, metadata, queries over files or logs. ↳ Virtual file system with folders, files, and permissions ↳ In memory file tree that behaves like a simple cloud drive ↳ Command line style navigation with support for going back to the previous folder Continued ↓ – P.S: I’ve released an LLD Masterclass course recently. 1497+ students have already enrolled, it covers: OOPS, SOLID, Design Patterns & Exercises for LLD Interviews. It’s rated 4.6 on Udemy. Check it out: https://lnkd.in/grUVcqgT

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ChatGPT is executing a classic platform strategy, aiming to become the "prompt-based operating system" 🤯 Many vendors have tried the super-app concept (PhonePe, Tata Neu), but what ChatGPT is attempting is off the charts. The Vision: 📱 "Ask / Browse / Learn anything within ChatGPT" - Search / Browser 🛒 "Buy anything within ChatGPT" - Commerce / Fashion ⚡ "Prompt / Talk-to to get things done" - Productivity 🔗 "Run your business within ChatGPT" - CRM / Sales At their recent DevDay, OpenAI dropped the real game-changer that makes all this possible. 📲 Apps Inside ChatGPT: Forget app stores. Developers can now build their apps directly inside ChatGPT. Imagine using Canva, booking a flight, or listening to Spotify just by talking to ChatGPT. Major partners like Spotify, Figma and Coursera are already on board. What does this actually mean? ChatGPT is collapsing the roles of an operating system, an app store, and a personal assistant into one single, conversational platform. With over 800 million weekly users, the magnetic pull for developers to join is undeniable 🚀 Competition? Nowhere near when it comes to consumer market share. Gemini, Anthropic, Copilot and Perplexity will get their shares, but ChatGPT is leading the race. The Big Question: What happens when a single vendor essentially owns the "everything app"? 🤔 What's Next (my prediction)? ⏭️ Hardware that integrates with the ChatGPT ecosystem of apps. Probably a fork of Android that would be more 'prompt/talk-to' focused over the traditional touchscreen interface. Exciting times ahead! 🔮 I won't be surprised if Android smartphones sold next year advertise "Supports ChatGPT 2.0." Why 2.0? I have a strong feeling that ChatGPT apps will definitely require breaking out of the standard Android application sandbox.

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👨‍🏫You’re not just up against folks with less talent than you—you’re facing those who are more dedicated. If you’re not fully locked into mastering system design, someone who is will snag that FAANG job. If you’re not all-in on DSA, another coder will claim the role you’re eyeing. If you’re not pouring effort into building projects, someone else’s portfolio will steal the spotlight. If you’re not obsessed with debugging, another developer will outpace you. 𝗧𝗵𝗲 𝗰𝗼𝗱𝗲𝗿 𝘄𝗵𝗼: • Builds side projects on weekends while you’re on X • Tackles one more bug before bed while you binge shows • Digs into docs during breaks while you’re chatting • Grinds LeetCode on Sunday mornings while you sleep That’s your real competition. They’re not smarter—they’re just hungrier. Thousands are vying for the same jobs. Same degrees, same bootcamps, same tutorials. What sets you apart? Your drive. It’s not about being good enough—it’s about being committed enough. While you’re hesitating, someone else is grinding. What’s one thing you’ll go all-in on this week?

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I have researched lots of product-based companies like Amazon, Stripe, Netflix, Asana, Zomato, Goldman Sachs, Paytm, Reddit, Inc., etc. for SDE-1 and SDE-2 roles, and found that variations of these frontend system design questions show up repeatedly in interviews. 𝐅𝐫𝐞𝐬𝐡𝐞𝐫 𝐋𝐞𝐯𝐞𝐥 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 1. Design a reusable modal/dialog system for a React application. 2. Design a toast/notification system that works across the entire app. 3. Design a dynamic form builder with nested fields and validations. 4. Design a reusable table/grid component with sorting, filtering, and pagination. 5. Design a drag-and-drop Kanban board like Trello. 6. Design a multi-step wizard form with validation and progress tracking. 7. Design a sidebar/navigation system that supports dynamic menus. 8. Design a theme provider system for dark/light modes. 9. Render a list of 10k+ items efficiently in React. 10. Implement infinite scrolling for large datasets. 11. Prevent unnecessary re-renders in a complex React component tree. 12. Design a search bar with debouncing, suggestions, and result highlighting. 13. Design a reusable file upload component with drag-and-drop support. 14. Design a consistent form validation and error messaging system. 15. Implement keyboard navigation and accessibility for interactive components. 16. Design a responsive dashboard layout with reusable widgets. 17. Optimize images, fonts, and static assets for faster page load. 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐝 𝐋𝐞𝐯𝐞𝐥 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 1. Structure a large-scale React project used by multiple teams. 2. Design a shared component library used across multiple applications. 3. Architect a scalable state management solution for a complex frontend. 4. Design a feature flag / feature toggle system for frontend features. 5. Design a plugin-based dashboard where teams can add widgets. 6. Design a scalable grid/layout system for dashboards and admin panels. 7. Architect a micro-frontend system for multiple React applications. 8. Design a dashboard with resizable and customizable widgets. 9. Design a real-time chat or messaging UI. 10. Design the frontend architecture for a collaborative editor like Google Docs. 11. Implement API caching, retry, and error handling strategies. 12. Design optimistic UI updates for actions like likes, comments, and edits. 13. Implement offline-first support with local storage or IndexedDB. 14. Handle large nested API responses with proper data normalization. 15. Design a scalable internationalization (i18n) system. Over the past few months while preparing myself, I’ve been documenting these patterns, solutions, and interview workflows. Eventually it turned into a Frontend Interview Playbook, covering system design questions like these, machine coding patterns, debugging approaches, and how to use AI effectively during preparation. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗵𝗲𝗿𝗲: https://lnkd.in/d8vBd3_j Use code FRONT10 for 10% off.

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Confluent offers 40+ LPA fixed and 60+ LPA CTC for engineers with 1+ year of experience — and the interview process Looks similar to this. 𝗥𝗼𝘂𝗻𝗱 1 – 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 (𝗛𝗮𝘀𝗵𝗠𝗮𝗽 𝗟𝗼𝗴𝗶𝗰) The first round focused on designing a system that efficiently used HashMaps. It tested not only understanding of the data structure but also the ability to think in terms of scalability and performance. 𝗥𝗼𝘂𝗻𝗱 2 – 𝗗𝗦𝗔 (𝗦𝘂𝗱𝗼𝗸𝘂 𝗦𝗼𝗹𝘃𝗲𝗿) A classic backtracking problem, requiring recursive logic and careful handling of constraints. This round tested depth in algorithms and clean code structure. 𝗥𝗼𝘂𝗻𝗱 3 – 𝗗𝗲𝘀𝗶𝗴𝗻 + 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴 Another design problem involving HashMap-based logic, but simpler in flow. It checked consistency in approach and clarity of thought under time pressure. 𝗥𝗼𝘂𝗻𝗱 4 – 𝗛𝗥 & 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗥𝗼𝘂𝗻𝗱 This round focused on personality, mindset, and motivation. Questions around past failures and comebacks, strengths and weaknesses, and interest in Confluent’s tech stack were asked. It ended with a discussion on career goals, team culture, and a chance to ask questions back to the interviewer. At Confluent, cracking the interview isn’t just about solving problems — it’s about 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝗮 𝗯𝘂𝗶𝗹𝗱𝗲𝗿, 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹, and 𝗮𝗹𝗶𝗴𝗻𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘄𝗶𝘁𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴.

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One of the most common queries I receive is where to prepare for LLD. My answer to that is a bit surprising but I strongly feel it would definitely work. None of the material out there for LLD is useful for interviews and you read that right! Most of the material consists of repositories with a lot of files and a bunch of business logic, which is good for nothing in interviews. LLD interviews basically have four patterns. Pattern 1: Just discuss the class objects with multiple attributes and then discuss a high level interface and write the pseudocode. Pattern 2: Discuss the classes and attributes but this time write executable code with a simple high level interface and its implementation. Pattern 3: This is the most important pattern, where you need to handle concurrency in a multithreaded environment and write executable code. Of course here as well you need to define proper classes and interfaces. Pattern 4: This is very rare and is what you see in most courses. Sometimes this is called machine coding where you are given either an assignment or asked to change existing code or write a repository from scratch. This is mostly asked by early stage or established startups with 2 hr round but is very rare in MNCs. You can see that the common thing among all these patterns is having a proper class design and defining a good workflow (a good interface) for how the system is going to work. One more important thing that really matters is whether your code is extensible for future use cases or not. These aspects don’t need to be learned from courses because you can do it yourself. Pick up a problem, start defining your attributes, check if you can use threads and make it multithreaded or not, and verify whether it is extensible for future cases or not (defining a proper interface will definitely ensure extensibility). That’s all is what you need to do! Unlike DSA or HLD, LLD doesn’t have any proper standard questions. You can get any question on the fly and all you need is proper knowledge of using threads, defining proper attributes, and if possible using some good collections(to code faster in interviews). Looking into courses will take you nowhere because you have to train your brain to solve problems. Hence what I suggest is to pick up a problem, code the solution (I strongly suggest one interface and two or three classes), keep it simple and ask either ChatGPT or Gemini AI to review it and check how you can handle multithreading. Checking external courses will take you nowhere because it’s you who has to think of a solution and no problem statement in LLD is truly unique. One more thing is these courses show you something that you can never practically code in an hour! And ofcourse you don’t need to code that much in an LLD round! This is the experience I gained from giving 20+ LLD interviews but let me know in case you can add up something to this!

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Noticed a clean example of API-driven automation today. When a Swiggy/Zomato order switches to “Out for Delivery”, MyGate automatically creates a pre-approved entry — no manual approval needed. From an engineering POV, this is likely powered by: • Order state change → event emission • Authenticated API call from Swiggy/Zomato to MyGate (pre-approval endpoint) • Validation + idempotent processing on MyGate’s side • Instant sync with guard systems and resident apps What stands out is the design choice: • Event-driven over polling • Clear ownership of state • Optimizing for the happy path • Removing human intervention from a high-frequency flow For users, it’s invisible. For engineers, it’s a solid example of how well-designed APIs and distributed systems quietly improve real-world UX. #SoftwareEngineering #APIDesign #SystemDesign #EventDrivenArchitecture #BackendEngineering #EverydayTech

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Something I learned while building queue-based systems at Jio: Idempotency isn't optional in distributed systems -- it's survival. When you're processing 10M+ subscription events/month through RabbitMQ, duplicate messages WILL happen. Network retries, consumer restarts, broker redeliveries. Our solution: Redis-based deduplication keys with TTL. Every incoming message gets a hash. If the hash exists in Redis, skip processing. Simple, but it prevented duplicate customer charges. The lesson: Design for failure from day one. Your happy path will work. It's the retry path that breaks production. #BackendEngineering #DistributedSystems #NodeJS #SystemDesign

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"How much did I spend on food last month?" — I built an AI that answers this by fetching my e-statements from Gmail, all on its own. Every month, I have to manually go through my financial statements just to check a few details.Finding something simple means scrolling through pages of transactions and calculating totals on my own. It takes time and gets repetitive. So I automated the whole thing. I built three MCP (Model Context Protocol) servers that work together: 🔹 Gmail MCP — searches my inbox and downloads statement PDFs 🔹 Statement Processor MCP decrypts the PDF, auto-detects the statement type, extracts every transaction, categorizes them, and stores in a database 🔹 Finance MCP — answers spending questions using the stored data Model used: Qwen 2.5 — an open-source model running locally via Ollama. No cloud APIs. My financial data never leaves my machine. The smart part: when I ask about a month with no data, the AI automatically fetches the email → downloads the PDF → processes it → then answers. I didn't code this flow the model figures it out. Here's what I can ask: 💬 "What did I spend in January?" → $200 across 10 transactions 💬 "Compare December vs January" → Dec: $150 | Jan: $200 — up 33% 💬 "Break down by category" → Food: $60 | Shopping: $50 | Transport: $40 | Subscriptions: $30 💬 "Any recurring payments?" → Netflix ($20), Spotify ($10), Cloud Storage ($10) 💬 "Top merchants?" → Amazon ($50), Uber Eats ($40), Starbucks ($30) Three small servers, each focused on a single responsibility — orchestrated intelligently by the AI. No complex chaining logic. No hardcoded pipelines. Just tools and an LLM that decides the workflow. That’s what MCP enables: natural tool composition. You define the capabilities — the AI figures out how to use them together. https://lnkd.in/gwMUVaEE #MCP #AI #LLM #BuildInPublic #ModelContextProtocol

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Just finished reading "The Accidental CTO." by Subhash Choudhary A system design masterpiece. It shows how Dukaan® scaled from zero to millions of stores just by learning fast and fixing what broke. If you’re a fresher interested in understanding how systems actually work at scale, this is gold. Elasticsearch, CDN, Docker, Kubernetes, Kafka—all explained with simple, real-life context. Highly recommended https://lnkd.in/grFAMya9 #SystemDesign #SoftwareEngineering #TechBooks #Startups #LearningInPublic #Freshers #EngineeringLife

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System Design is not something you only worry about once you hit Senior SDE, trust me 👇 Because in reality, System Design shows up much earlier, and it’s a very imp. filter for any roles beyond SDE-2/3. That’s why I’m sharing this “System Design Made Easy” doc 📄 Big thanks to Bosscoder Academy for curating this structured guide! 🙌 📌 Check their programs here → bcalinks.com/Rr38sAy Here’s what you’ll find inside the doc: → Core concepts explained step by step (scalability, caching, load balancing, CAP theorem, databases) → Breakdowns of real-world system architectures → A 60-day roadmap designed to fit alongside a full-time job If you’re a mid-to-senior SDE prepping for your next role, this doc can save you weeks of scattered prep and give you a roadmap that actually works. And if you’re serious about growing into Sr. SDE roles at top PBCs, Bosscoder Academy’s EVOLVE program for SDEs with 6+ YOE is built exactly for this. They’ve already helped 2200+ engineers crack top PBC roles, maybe you’re next? #systemdesign #seniorsde #techinterviews #growth #jobswitch #collab

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Why to start using Bun after years with pnpm / yarn. For the longest time, happily using pnpm and yarn in projects great tools, fast installs, solid workspace support, and super reliable. But as stacks moved faster and we started writing more TS/JS across backend + frontend, pain of juggling so many separate tools just to get a project up and running. Here’s the honest takeaway: Bun isn’t just another tool it’s four tools in one runtime, package manager, test runner, and bundler all shipped in a single binary. What really like about Bun so far - • Speed - package installs are genuinely fast (many installs finish in seconds vs minutes) thanks to Bun’s efficient design. • Zero config TypeScript/JSX - running .ts, .tsx, or .jsx files works out of the box without extra build steps. • Unified tooling - don’t need to manage separate tools for bundling, testing, and installs bun test, bun build, bun install, etc. all work consistently. Things we need to still watch - Bun is still maturing and some edge cases around Node compatibility can pop up so I am cautious about using it for every project just yet. But for new or experimental work, it’s been a real productivity booster in my day-to-day. #javascript #typescript #webdevelopment #bunjs #developerexperience #nodejs #tooling #opensource

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