Building a JSON Parser from Scratch: A Step-by-Step Guide

3 years ago, I wrote my first API. It worked. Barely. No error handling. No input validation. Hardcoded values everywhere. I was just happy it returned a 200. Fast forward to today - I've shipped APIs in production that handled real client data, prevented revenue losses, and a API that directly convinced a client to onboard. Here's what I wish someone had told me at the start: 1. "It works on my machine" is not done. Done means it works under load, with bad inputs, with network failures, with edge cases you didn't think of. I learned this the hard way. 2. Naming things well is a superpower. The biggest time sink in early code isn't logic - it's trying to understand what past-you was thinking. Write for the next developer, not the compiler. 3. You will touch the database in production. And it will be terrifying the first time. Learn SQL properly. Understand indexes. Respect transactions. I've fixed bugs at the DB level that would have taken down a live client system. 4. Pick boring technology first. I chased new tools early. Then I spent a week building a document processing POC under a tight deadline - and the tools that saved me were the ones I already knew deeply: NestJS and solid API design. Familiarity under pressure is an unfair advantage. 5. Ship something real as fast as you can. Side projects are great. But nothing teaches you faster than code that actual users depend on. The feedback loop is brutal and honest. The gap between "it works" and "it's production-ready" is where most of the real learning happens. Still learning. Always will be. What's one thing you wish you knew when you wrote your first API? Drop it below 👇 #softwaredevelopment #webdevelopment #reactjs #nodejs #apidesign #fullstackdeveloper #devjourney #programming

𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗖𝗼𝗽𝘆 𝘃𝘀 𝗗𝗲𝗲𝗽 𝗖𝗼𝗽𝘆 📦 If you’ve ever updated state and something weird happened… this might be why 👇 🔹 Shallow Copy → copies only the first level 🔹 Nested objects are still referenced (same memory) Example: ➡️ Using { ...obj } or Object.assign() 💡 Problem: Change a nested value… and you might accidentally mutate the original object 😬 🔹 Deep Copy → copies everything (all levels) 🔹 No shared references 🔹 Safe to modify without side effects Example: ➡️ structuredClone(obj) ➡️ or libraries like lodash ⚠️ The common pitfall: You think you made a copy: ➡️ { ...user } But inside: ➡️ user.address.city is STILL linked So when you update it: ❌ You mutate the original state ❌ React may not re-render correctly ❌ Bugs appear out of nowhere 🚀 Why this matters (especially in React): State should be immutable ➡️ Always create safe copies ➡️ Avoid hidden mutations ➡️ Keep updates predictable 💡 Rule of thumb: 🔹 Flat objects? → shallow copy is fine 🔹 Nested data? → consider deep copy Understanding this difference = fewer bugs + cleaner state management And yes… almost every developer gets burned by this at least once 😄 Sources: - JavaScript Mastery - w3schools.com Follow 👨💻 Enea Zani for more #javascript #reactjs #webdevelopment #frontend #programming #coding #developers #learnjavascript #softwareengineering #100DaysOfCode

Building scalable, decoupled architectures requires a deep understanding of the underlying mechanics—not just relying on framework magic. I recently deployed a new module within my open-source Django_WebFramework_RD_Lab. The goal was to build a strict, end-to-end testing environment to explore RESTful API interactions, relational data modeling, and cross-origin resource sharing (CORS) from the ground up. Here is a technical breakdown of the architecture and the challenges solved: ⚙️ Backend Engineering (Python / DRF) Architecture: Shifted away from generic ViewSets to strictly utilize Class-Based Views (APIView) for granular, explicit control over HTTP methods and response handling. Data Modeling & Validation: Implemented 1:N relational modeling (Movies to User Ratings). Built custom serializer validation to handle edge cases, such as preventing duplicate reviews and gracefully handling empty querysets (returning 200 OK with empty lists instead of 400 Bad Request). 🖥️ Frontend Integration (Vanilla JS SPA) The Client: Rather than masking the API consumption behind a heavy framework like React or Vue, I built a lightweight, dependency-free Single Page Application using vanilla JavaScript, HTML, and CSS. The Goal: This served as a pure, transparent client to test the Fetch API, asynchronous state management, and strict CORS policies across different origins. 🚀 Deployment & DevOps Hosting: Successfully deployed the full stack on PythonAnywhere. Configuration: Managed WSGI server configurations and isolated virtual environments (Python 3.12). Security: Implemented python-dotenv to securely manage environment variables, ensuring sensitive configurations like SECRET_KEY and ALLOWED_HOSTS remain out of version control. Next up in the lab: transitioning these architectural patterns to explore asynchronous performance and high-concurrency backends. Explore the Lab: 🟢 Live Interactive Dashboard: [https://lnkd.in/gzUSDUNd] 🔗 Repository & ER Diagrams: [https://lnkd.in/gc_jg87n] I’d love to hear from other backend engineers—what are your preferred strategies for managing complex nested serializers in DRF? #Python #SoftwareEngineering #BackendDevelopment #DjangoRESTFramework #SystemDesign #APIArchitecture #RESTAPI

Read this today: https://lnkd.in/g2SMqkHa and the HN thread that followed https://lnkd.in/gTSx42V8 The headline people keep repeating is “AI rewrote 100k lines of code.” That’s not what happened. A TypeScript system already existed. It worked. That part gets weirdly minimized, but it’s doing most of the heavy lifting. What actually ran was a loop. 1)Translate to Rust 2) Run both versions. 3) Compare outputs. 4) Feed the diff back. 5) Try again. Over and over. Rinse lather and repeat for weeks. It’s not intelligence. It’s search with a scoreboard. The model isn’t sitting there “understanding” the system. It’s making moves, getting told “wrong,” and adjusting until the failures stop showing up. That’s enough if your feedback is sharp. And that’s the real constraint. This only works because correctness was measurable. Same inputs produced comparable outputs. Failures were visible. The system didn’t depend on fuzzy judgment calls or “this feels right” decisions. You either matched behavior or you didn’t. Take that away and this whole thing collapses. Also… this wasn’t hands-off. There was constant steering. Resetting when things drifted. Deciding when something was acceptable vs subtly broken. It’s closer to supervising a very fast intern than replacing an engineer. One detail from the post that stuck with me, the code didn’t get better. No new architecture. No clever redesign. No meaningful optimization passes. Just a persistent grind toward equivalence. Translation under pressure. HN had a lot of debate about whether this counts as “real reasoning.” tbh I don’t think that matters much. What matters is the workflow. If you can define correctness tightly enough, you can turn parts of programming into a search problem and let the machine chew through it. If you can’t, you’re still doing it the old way. Careful thinking, ambiguity, tradeoffs, all the annoying human stuff. Small shift, but it feels important. The bottleneck isn’t writing code as much anymore. It’s being able to say, with zero wiggle room, “this is correct.”

𝗛𝗼𝘄 𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗪𝗼𝗿𝗸𝘀 𝗶𝗻 𝗡𝗼𝗱𝗲.𝗷𝘀 As developers, we often focus on writing efficient code—but what about memory management behind the scenes? In 𝗡𝗼𝗱𝗲.𝗷𝘀, garbage collection (GC) is handled automatically by the 𝗩𝟴 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲, so you don’t need to manually free memory like in languages such as C or C++. But understanding how it works can help you write more optimized and scalable applications. 𝗞𝗲𝘆 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: 𝟭. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 Whenever you create variables, objects, or functions, memory is allocated in two main areas: Stack→ Stores primitive values and references Heap→ Stores objects and complex data 𝟮. 𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 (𝗠𝗮𝗿𝗸-𝗮𝗻𝗱-𝗦𝘄𝗲𝗲𝗽) V8 uses a technique called Mark-and-Sweep: * It starts from “root” objects (global scope) * Marks all reachable objects * Unreachable objects are considered garbage * Then, it sweeps (removes) them from memory 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 Not all objects live the same lifespan: Young Generation (New Space) → Short-lived objects Old Generation (Old Space) → Long-lived objects Objects that survive multiple GC cycles get promoted to the Old Generation. 𝟰. 𝗠𝗶𝗻𝗼𝗿 & 𝗠𝗮𝗷𝗼𝗿 𝗚𝗖 Minor GC (Scavenge)→ Fast cleanup of short-lived objects Major GC (Mark-Sweep / Mark-Compact) → Handles long-lived objects but is more expensive 𝟱. 𝗦𝘁𝗼𝗽-𝘁𝗵𝗲-𝗪𝗼𝗿𝗹𝗱 During GC, execution pauses briefly. Modern V8 minimizes this with optimizations like incremental and concurrent GC. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗠𝗲𝗺𝗼𝗿𝘆 𝗜𝘀𝘀𝘂𝗲𝘀: * Memory leaks due to unused references * Global variables holding data unnecessarily * Closures retaining large objects 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: * Avoid global variables * Clean up event listeners and timers * Use streams for large data processing * Monitor memory using tools like Chrome DevTools or `--inspect` Understanding GC = Writing better, faster, and scalable applications #NodeJS #JavaScript #BackendDevelopment #V8 #Performance #WebDevelopment

"We did a deep dive into TypeScript advanced generics in 30 different projects. The results? A 40% reduction in runtime errors." Diving headfirst into a complex codebase, I found myself puzzled over a brittle system that suffered from frequent failures and cumbersome maintenance. The culprit was a lack of strong type constraints, hidden inside layers of JavaScript code that attempted to mimic what TypeScript offers natively. The challenge was clear: harness the power of TypeScript's advanced generics and inference to refactor this tangled web. My first task was to unravel a central piece of the system dealing with API data structures. This involved migrating from basic `any` types to a more robust setup using TypeScript's incredible type-level programming capabilities. ```typescript type ApiResponse<T> = { data: T; error?: string; }; type User = { name: string; age: number }; function fetchUser(id: string): ApiResponse<User> { // Implementation } // Correct usage leads to compile-time type checks instead of runtime surprises const userResponse = fetchUser("123"); ``` The initial refactor was daunting, but as I delved deeper, vibe coding with TypeScript became intuitive. The compiler caught more potential issues at design time, not just in this module but throughout the entire application as types propagated. The lesson? Properly leveraging TypeScript's type-level programming can transform your maintenance nightmare into a well-oiled machine. It requires an upfront investment in learning and applying generics, but the returns in stability and developer confidence are unmatched. How have advanced generics and inference changed your approach to TypeScript projects? #WebDevelopment #TypeScript #Frontend #JavaScript

🚀 Week 6 Backend Dev Challenge: Regular Expressions(Regex) This week, I worked on something that felt both nerdy and surprisingly exciting: validating a credit card number using JavaScript. Yes I know, credit card validation doesn’t sound exciting at first… but once you dive into Regular Expressions, your brain suddenly enters “detective mode.” 😅 I had a Verve card lying around so I decided to use it for this. I made some research and found out Verve cards had different prefixes. Some started with 5060, 5078 and 6500. To validate the card, I had to use a regex pattern. Think of regex as that strict friend who checks every tiny detail before letting anyone into the party. 😂 The pattern I used: ^(5060|5078|6500)[0-9]{12,13}$ What it does: ✅️Makes sure the card number starts with a valid Verve prefix ✅️Ensures the rest are numbers only ✅️Checks that the length is just right and blocks anything that looks suspicious 👀 It’s like building a mini-security gate with code. Instead of writing just a random function, I challenged myself to use Object-Oriented Programming which I’ve been learning recently. So I created a class, added properties, and built a validate() method inside it. Suddenly, my little validator felt more like a program and less like a quick hack. The cool part? Using OOP made it super easy to create multiple card objects: card1 → valid card2 → invalid Each one tested itself, like they had their own personalities. OOP really helps you write cleaner, more organized, and more scalable code. I finally get why people hype it so much. 😄 Honestly, this task made me appreciate how much detail goes into something as “simple” as validating a card number. #JavaScript #LearnToCode #Regex #OOP #CodingJourney #BackendDev

If you're still using JSON.stringify(a) === JSON.stringify(b) for deep comparisons, you're leaving performance on the table. It works - until it doesn't. Key ordering differences, undefined values, and circular references will silently break your logic in production. Here are real alternatives worth benchmarking: - structuredClone + manual check: fast for simple objects - Lodash isEqual: reliable, handles edge cases, battle-tested - fast-deep-equal: consistently wins benchmarks for plain objects A quick real-world example: import isEqual from 'fast-deep-equal'; const prev = { user: { id: 1, roles: ['admin'] } }; const next = { user: { id: 1, roles: ['admin'] } }; isEqual(prev, next); // true, no stringify tricks needed fast-deep-equal is roughly 4-8x faster than JSON.stringify in most benchmark suites, especially with nested structures. Practical takeaway: Pick your comparison tool based on data shape. Use fast-deep-equal for plain objects, Lodash isEqual when you need Date, RegExp, or Map support. What comparison method are you currently using in your React or Node projects - and have you actually benchmarked it? #JavaScript #WebDevelopment #FrontendDevelopment #JSPerformance #NodeJS #CodeQuality

Most developers learn languages. Few understand why categories of languages exist. That difference shows up in how systems are designed. The image breaks it into 3 types — scripting, programming, markup. Useful… but also slightly misleading if you stop there. Let’s refine it 👇 1. Scripting languages (Python, JavaScript, PHP, Ruby) Not “just automation.” They are: • Orchestration layers • Glue between systems • Rapid experimentation tools 👉 Insight: The best engineers don’t overuse them for scale-heavy systems—but they absolutely use them to move fast and validate ideas. 2. Programming languages (Java, C#, C++, Go, etc.) This is where architecture discipline lives. • Memory management decisions • Concurrency models • Performance trade-offs 👉 Hard truth: Many developers jump here too early without understanding system design. Language choice doesn’t fix poor architecture. 3. Markup languages (HTML, XML, etc.) Often underestimated. But they define: • Structure of user experience • Data contracts • Interoperability standards 👉 Reality check: Poor markup = broken accessibility, SEO, and data integrity. What most people miss Languages are not just tools. They represent layers of abstraction in a system: • Markup → Structure • Scripting → Flow • Programming → Control If you don’t understand the layer you’re working in, you’ll solve the wrong problem… efficiently. Practical tips for developers • Don’t pick a language—pick the problem domain • Use scripting for speed, not for long-term system complexity • Master one systems language deeply before hopping stacks • Treat markup as part of engineering, not “frontend fluff” • Think in layers, not languages The real skill isn’t knowing 10 languages. It’s knowing when NOT to use one. #SoftwareEngineering #Programming #Developers #Coding #SystemDesign #CleanArchitecture #TechLeadership #DigitalEngineering #WebDevelopment #Backend #Frontend #LearningToCode #DeveloperMindset

🧠 Promises made async code better… But Async/Await made it feel like synchronous code. 🔹 What is Async/Await? - It’s a cleaner way to write Promises. - async → makes a function return a Promise - await → pauses execution until the Promise resolves 🔹 Example (Without Async/Await) fetch("api/data") .then((res) => res.json()) .then((data) => console.log(data)) .catch((err) => console.log(err)); 🔹 Same Example (With Async/Await) async function getData() { try { const res = await fetch("api/data"); const data = await res.json(); console.log(data); } catch (err) { console.log(err); } } 🔹 Why Async/Await? ✅ Cleaner & more readable ✅ Looks like normal (sync) code ✅ Easier error handling with try...catch 💡 Key Idea - Async/Await is just syntactic sugar over Promises. 🚀 Takeaway - async returns a Promise - await waits for it - Makes async code simple & readable Next post: Fetch API Explained Simply 🌐 #JavaScript #AsyncAwait #Promises #Frontend #WebDevelopment #LearnJS #Programming #LearningInPublic