Upgrade Node API Performance with Parallel Fetching

I got tired of debugging API issues with console.log(). After one too many late nights staring at: POST /api/orders 500 8ms ...with zero context about what was in the request body,  what the response said, or which user triggered it —  I built something better. Introducing reqlog — a live HTTP request dashboard for  NestJS, Express and Fastify. Drop it in as middleware and a dashboard opens at  localhost:9000 showing every request in real time: → Full request + response body → Payload diff between requests → One-click request replay → Slow request highlighting → Zero config, zero persistence NestJS setup is literally 3 lines: import { ReqlogModule } from 'reqlog-nestjs' @Module({ imports: [ReqlogModule.forRoot()] }) // dashboard opens automatically I use this daily at Softylines across 45+ microservices. It replaced a debugging workflow that was costing my team hours every week. MIT licensed. Open source. Free forever. GitHub → https://lnkd.in/dzXM6BrK If you've ever added a console.log at 11pm in frustration — this one's for you. ⭐ #nodejs #nestjs #opensource #javascript #devtools #webdev

𝗦𝗶𝗻𝗰𝗿𝗼𝗻𝗼, 𝗔𝘀𝘀𝗶𝗻𝗰𝗿𝗼𝗻𝗼, 𝗮𝗻𝗱 𝗔𝘀𝘆𝗻𝗰/𝗔𝘄𝗮𝗶𝘁: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗻𝗴 𝗧𝗵𝗲 𝗗𝗼𝘁𝘀 You need to understand how JavaScript works, especially with Node.js. Node.js handles tasks that take time, like HTTP requests or database access. Synchronous means instructions are executed one after the other, in order. It's like a line of people being served by one person at a supermarket. The next person is served only after the previous one is done. Asynchronous means the program doesn't wait for the operation to finish. It moves on to the next instruction, and the asynchronous operation happens in the background. Node.js is single-threaded, but it can handle thousands of tasks without blocking the Event Loop. This is because of asynchronous operations. The await keyword is used inside asynchronous functions. It waits for a Promise to resolve before continuing the code execution, without blocking the main thread. When you use await, the Node.js execution is paused, but it doesn't block the CPU or the main thread. The context of the function is saved, and the Event Loop continues processing other tasks. In short, await makes asynchronous code look like synchronous code, but it keeps the advantages of asynchronous execution. It makes the code more readable and less complex. Source: https://lnkd.in/gJYhvzvD

𝗦𝗶𝗻𝗰𝗿𝗼𝗻𝗼, 𝗔𝘀𝘀𝗶𝗻𝗰𝗿𝗼𝗻𝗼, 𝗮𝗻𝗱 𝗔𝘀𝘆𝗻𝗰/𝗔𝘄𝗮𝗶𝘁: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗻𝗴 𝗧𝗵𝗲 𝗗𝗼𝘁𝘀 You need to understand how JavaScript works, especially with Node.js. Node.js handles tasks that take time, like HTTP requests or database access. Synchronous means instructions are executed one after the other, in order. It's like a line of people being served by one person at a supermarket. The next person is served only after the previous one is done. Asynchronous means the program doesn't wait for the operation to finish. It moves on to the next instruction, and the asynchronous operation happens in the background. Node.js is single-threaded, but it can handle thousands of tasks without blocking the Event Loop. This is possible with asynchronous operations. The await keyword is used inside asynchronous functions. It waits for a Promise to resolve before continuing the code execution, without blocking the main thread. When you use await, the Node.js execution is paused, but it doesn't block the CPU or the main thread. The context of the function is saved, and the Event Loop continues processing other tasks. In summary, await is a way to "wait without blocking". It makes asynchronous code look like synchronous code, pausing the execution only until a Promise is resolved. Source: https://lnkd.in/gJYhvzvD

The evolution of TypeScript post-2020 has been all about refining Developer Experience (DX) and type exactness. One of the most powerful recent features (since TS 4.9) is the satisfies operator. The Problem: Standard type annotations (e.g., const color: Color = "re";) are excellent for general types. However, if your union type includes generic strings (type Color = "red" | "green" | string;), TypeScript becomes too loose. A typo like "re" will compile successfully, leading to invisible runtime errors when you expect strictly "red". The Satisfies Shift: ✨ Exactness Check: The satisfies operator ensures that a value satisfies the required shape, while preserving the most precise type possible. ✨ Catches Typos Early: If you write "re" satisfies Color;, TypeScript will flag "re" as an error even if generic string is allowed in the union. It knows "re" is not exactly one of the known precise options. ✨ Type System Precision: We are moving from "general types" to "exact types," ensuring our compiler is working harder for us, not just accepting general inputs. The infographic below visualizes this exactness problem and solution. Are you using satisfies to catch these kinds of subtle bugs? #TypeScript #SoftwareEngineering #JavaScript #CodingTips #DeveloperExperience #TypeSafety #CleanCode #WebDevTips #Productivity

𝗧𝗵𝗲 𝗖𝗹𝗮𝗎𝗱𝗲 𝗖𝗼𝗱𝗲 𝗦𝗼𝘂𝗿𝗰𝗲 𝗖𝗼𝗱𝗲 𝗥𝗲𝘃𝗲𝗮𝗹𝘀 I read a reconstructed copy of Claude Code's source code. Here's what I learned: - The codebase is large, with 1,900 files and 510,000 lines of code. - It's built with TypeScript, Bun, and a React/Ink-style stack for the terminal UI. - The system instruction layer is present in the client-side code, with runtime context injected into it. - The code shows a significant amount of prompt logic on the client side, not just on the server side. - The design has a sharp distinction between exposed tools and internal tools. - The tool list is aligned with prompt caching, suggesting that fewer tools can lead to more stable behavior. - The shell execution layer is memorable, with commands categorized and guarded by safety checks. - Comments explain not only what the code does, but why certain decisions were made. - The code is tuned for performance, with explicit side effects and parallelization to reduce startup latency. You can learn from reading code like this. But remember, "readable" and "freely usable" are not the same thing. Source: https://lnkd.in/gtJ_Z_rz

Week 23: Going Deep into Redux — Store, Middleware, Thunks & DevTools This week, I focused on going beyond surface-level Redux usage and understanding how state management works in real-world applications. Instead of just connecting components and dispatching actions, I explored the full Redux architecture and learned how to structure scalable, maintainable applications. --- What I Learned 🧠 Redux Store — The Brain of Your App The store is the single source of truth. It holds all global state, provides dispatch() for updates, and ensures predictable state flow. Once I understood this, connecting components and reasoning about data became much easier. --- ⚡ Action Creators & Dispatch Action creators help standardize actions and reduce repetition. dispatch() is the only way to update state, making updates traceable and predictable. This week, I practiced using them effectively in real features. --- 🗂️ Scalable File Structure I experimented with organizing Redux code in a modular way: store.js for configuration Feature-based folders for slices/reducers This makes the app more maintainable and easier to scale. --- 🔌 Middleware & Thunks Middleware sits between dispatch and reducers. It allows for logging, debugging, and handling async logic. Thunks make API calls and async workflows manageable inside Redux without cluttering components. --- 🌐 API Calls via Redux I practiced moving API logic from components to Redux, handling: Loading states Success responses Errors This centralized data management improves both clarity and maintainability. --- 🛠️ Redux DevTools — Debug Like a Pro Redux DevTools made a huge difference. I could see: Every dispatched action Previous and next state Full action history Time-travel debugging allowed me to pinpoint where issues occurred — no guesswork, just clarity. --- Key Takeaways Redux enforces a predictable data flow Middleware extends functionality beyond basic state updates Thunks are essential for real-world async handling Proper structure improves scalability Redux DevTools makes debugging efficient and visual --- Next Steps Next week, I plan to explore Redux Toolkit to simplify many of these patterns and reduce boilerplate. --- If you’re learning Redux, my advice: Don’t just write reducers — understand the flow, explore middleware, and debug with DevTools. That’s when Redux truly starts making sense. #react #redux #frontend #javascript #webdevelopment #learninginpublic

One of the most powerful concepts behind Node.js is the Event Loop. And honestly, this is where many beginners get confused. Node.js is single-threaded, which means it uses one main thread to handle operations. But then how does it handle multiple requests at the same time? That is where the Event Loop comes in. The Event Loop allows Node.js to perform non-blocking operations. Instead of waiting for tasks like database queries or API calls to finish, Node.js: • Registers the task • Moves on to handle other requests • Executes the callback when the task is complete This makes Node.js highly efficient and scalable. A simple flow: Request comes in → Task is sent to background (Web APIs / system) → Node continues processing other requests → Callback is added to queue → Event Loop picks it up and executes it This is why Node.js is perfect for: • Real-time applications • APIs handling multiple users • Scalable backend systems Understanding the Event Loop is what separates basic Node.js usage from real backend engineering. Because performance is not just about code. It is about how your system handles concurrency. #Nodejs #EventLoop #BackendDevelopment #FullStackDeveloper #JavaScript #WebDevelopment #MERNStack #SoftwareEngineer #ScalableSystems #PersonalBranding #moizycodes

TypeScript's utility types are a powerful feature that can enhance the robustness and maintainability of your code. One common challenge developers face is managing complex object types, particularly when dealing with APIs or intricate data structures. Utility types like Partial, Pick, and Record can simplify these issues immensely. For instance, using Partial allows you to create a type with all properties of a given type set to optional, which is particularly useful for form submissions where not all fields may be required. Consider this scenario: you have a User type with several properties. If you want to create an update function that allows users to change their data without needing to provide all fields, using Partial<User> can alleviate this concern. Here's a quick example: ```typescript type User = { name: string; email: string; age: number; }; function updateUser(id: number, userData: Partial<User>) { // Update user logic here } ``` While the benefits of using utility types are clear, they do come with considerations. Overuse can lead to ambiguity in your type definitions, making the code harder to read and understand. Balancing clarity with the flexibility that utility types offer is crucial. Ensuring that your type definitions remain intuitive aids in maintaining code quality over time. Pros: Enhances code reusability and reduces boilerplate. Cons: Possible confusion in type definitions if used excessively. #TypeScript #UtilityTypes #CodeQuality #AdvancedTypescript

Just built a web application — a simple document approval web application where users can submit docs, invite reviewers, and make approval decisions on review requests. Built the backend in Go with MySQL as the database, GORM for database access, and Uber's Fx to keep the API modular. The frontend built in React Vite + TypeScript. Also use OpenCode as AI agent tool to handle some repetitive boilerplate codes Check out the project on GitHub: https://lnkd.in/gxrC6W7R

Small JavaScript bugs keep escaping to production and breaking critical user flows. Debugging inconsistent runtime behavior steals time from feature delivery. ────────────────────────────── Record Type for Object Maps TypeScript catches type mismatches during development before runtime. #typescript #record #maps #objects ────────────────────────────── Core Concept TypeScript catches type mismatches during development before runtime. Key Rules • Use strict mode and avoid any in business logic. • Model API responses with exact interfaces. • Use unknown at boundaries, then narrow deliberately. 💡 Try This type Status = 'open' | 'closed'; function isOpen(s: Status) { return s === 'open'; } console.log(isOpen('open')); ❓ Quick Quiz Q: When should unknown be preferred over any? A: At external boundaries where validation and narrowing are required. 🔑 Key Takeaway Strong typing turns refactors from risky guesswork into confident change.