Deployed something today that finally makes this project feel real. Built the swipe system + personalized job feed What actually went in: Node.js + MongoDB Cursor-based pagination (no more repeated jobs) Indexing (so it doesn’t die at scale) Swipe API → stores likes/dislikes Match logic → adjusts ranking based on behavior Pain point: Pagination + filtering broke the feed multiple times. Either no jobs or same jobs again. Fix: Reworked query logic + added proper indexing (IXSCAN finally showing up ) Attached is the MongoDB explain tree this is where it clicked that performance is actually working. Still basic: Right now it only learns from exact job swipes. Next step is making it understand similar roles.

Built a URL Shortener from scratch today 🔗 Nothing fancy, but as a student still finding my footing in backend development, this one felt good to complete. What went into it: → Node.js + Express for the server → MongoDB + Mongoose for storing URL mappings → nanoid for generating unique short IDs → REST API design (POST to shorten, GET to redirect) Small project, big lessons — async/await, middleware, dynamic routing, and why order of code actually matters Code here if you want to check it out 👉 https://lnkd.in/geZNtcVu Still learning every day. If you're a fellow student building small projects and figuring things out as you go — keep going, it adds up. #NodeJS #MongoDB #WebDevelopment #100DaysOfCode #StudentDeveloper #BackendDevelopment

Most developers write MongoDB queries. Very few optimize them for production. That gap creates: • Slow APIs • High DB load • Scaling issues So I built a MongoDB Query Analyzer. It doesn’t just find slow queries — it tells you why they are slow and how to fix them. From index suggestions to query improvements, it’s designed for real production problems. If you're working on a high-traffic system, this might save you hours of debugging.

I built a simple key-value store (Node.js + TypeScript) to understand how databases work under the hood. I started as just Map wrapper and turned into: - write-ahead logging (WAL) - snapshots + compaction -TTL using a min-heap - a basic TCP protocol A few things clicked while building this: - In-memory is easy. Durability is not. - Every performance gain comes with a tradeoff - Logs grow forever unless you manage them - Even TTL needs the right data structure to scale This is far from production-ready (no replication, no strong durability guarantees), but that wasn’t the goal. The goal was to learn by building. And it definitely changed how I look at systems like Redis and RocksDB.

📚 Build a Book Store API (CRUD + MongoDB Aggregations) Most backend projects stop at CRUD. But real-world APIs go beyond just storing and fetching data. After 2.5+ years working with Node.js, I revisited a simple Book Store API — and turned it into something closer to a production-ready system. 🔧 What’s inside: • CRUD operations with MongoDB • Aggregation pipelines for advanced queries • Search, filtering & pagination • Clean and structured API responses 💡 The Upgrade: 👉 Instead of just “Get Books”, the API can now: • Filter by category, price, author • Generate insights using aggregations • Handle large datasets efficiently That’s the shift from: Project → Real Backend System If you're building APIs, this is where things start getting interesting. 🔗 Full implementation & breakdown: https://lnkd.in/gcFKy34R #NodeJS #MongoDB #BackendDevelopment #APIDesign #SoftwareEngineering #WebDevelopment #DatabaseDesign

Today I focused on optimizing authentication performance in my Django backend. Instead of relying on the default JWT flow (which hits the database on every request), I implemented a custom authentication class with a Redis caching layer. Now the flow looks like this: • JWT is verified (no database call) • User data is fetched from Redis cache • Database lookup is skipped for most requests • Token blacklist is checked to handle logout/revocation This small change significantly reduces database load and makes the system more scalable as traffic grows. I also added a fallback to the default behavior in case the cache misses, so the system remains reliable. It’s one of those improvements that users don’t see directly, but it makes a huge difference in performance under real usage. Next step: extending this to handle permissions and roles more efficiently.

A developer with 0 users debating MongoDB vs PostgreSQL is my favourite type of person. Bro you have 3 users. Your mom, your best friend, and you testing it in incognito. The database is not your problem. I have built 30+ products. You know what killed most of them? Not the wrong database. Not the wrong framework. Not tabs vs spaces. It was me. Spending 3 weeks picking the perfect tech stack for an app that 4 people would use. I once spent an entire weekend debating microservices vs monolith for a side project that got 11 downloads. 9 of which were me checking if it worked. Real talk: your first 1000 users cannot tell if you used Postgres or SQLite. They just want the button to work. Pick what you know. Ship the thing. Get actual users. Then, and only then, you have earned the right to have this debate. Until then you are just two people arguing about leg room on a flight neither of you has booked.

Adding a database to your stack shouldn't be a chore, which is why we've streamlined the MongoDB Node.js driver experience for maximum efficiency. This 60-second walkthrough demonstrates how to pull in the driver via npm, connect to Atlas, and execute find operations with minimal code. It’s a clean, straightforward process that ends with a simple terminal command to see your results in real-time. If you're looking for the path of least resistance for your next project, this is it. https://lnkd.in/gEhZaDXe

Adding a database to your stack shouldn't be a chore, which is why we've streamlined the MongoDB Node.js driver experience for maximum efficiency. This 60-second walkthrough demonstrates how to pull in the driver via npm, connect to Atlas, and execute find operations with minimal code. It’s a clean, straightforward process that ends with a simple terminal command to see your results in real-time. If you're looking for the path of least resistance for your next project, this is it. https://lnkd.in/gzJTMxE9

If you're building on MongoDB, you should be using our Agent Skills & MCP, here's where to start if you want to learn more: https://lnkd.in/gZEU-Ym8 If you want to jump in and install the plugins, here's the details: Claude Code: Open Claude and use this command: /plugin install mongodb@claude-plugins-official Cursor: Open cursor and use this command: /add-plugin mongodb Gemini CLI: Start a Gemini session and use this command: gemini extensions install https://lnkd.in/gkyq_g4Q VS Code Extension: Open Extension View, search for "MongoDB for VS Code", and install it. You get: - Agent Skills for MongoDB workflows and best practices - MCP Server for real-time, context-aware guidance Build, query, and iterate without leaving your tool of choice! Happy coding!