What This Repository Is A complete, structured, beginner-friendly Node.js learning path. 30 sessions. Each session has Clear learning objectives Step-by-step explanations Working code examples Practice exercises Interview questions Summary of key points No fluff. No assumptions. Just code. The Complete Curriculum Phase 1 - Node.js Fundamentals (Sessions 1-5) Session Topic What You Will Build 01 Introduction to Node.js Your first Node.js program 02 Project Setup and npm package.json, node_modules 03 How Node.js Works Event loop, blocking vs non-blocking 04 Modules and Imports Your first custom module 05 File System Module Read, write, update, delete files Sample code from Session 05 const fs = require ( " fs " ); // Create a file fs . writeFileSync ( " student.txt " , " Welcome To Node.js " ); // Read the file const data = fs . readFileSync ( " student.txt " , " utf8 " ); console . log ( data ); // Welcome To Node.js // Append to file fs . appendFileSync ( " student.txt " , " \n New line added " ); // Delete file fs . unlinkSync ( " student.txt " ); Phase 2 - Core Modules (Sessions 6-10) Session Topic What You Will Build 06 Path Module Cross-platform file paths 07 OS Module System information 08 Events and EventEmitter Custom event handling 09 HTTP Module Create a server 10 Multi-Route Server Multiple routes, JSON responses Sample code from Session 10 const http = require ( " http " ); const server = http . createServer (( req , res ) => { if ( req . url === " / " ) { res . end ( " Home Page " ); } else if ( req . url === " /about " ) { res . end ( " About Page " ); } else if ( req . url === " /products " ) { res . setHeader ( " Content-Type " , " application/json " ); res . end ( JSON . stringify ([{ id : 1 , name : " Laptop " }])); } else { res . statusCode = 404 ; res . end ( " Page Not Found " ); } }); server . listen ( 3000 ); Phase 3 - Building REST APIs (Sessions 11-15) Session Topic What You Will Build 11 CRUD with Dummy Data Complete REST API using array 12

Your API hands out 50 records at a time across 400 pages. You need all of them. You do not need them all at once. Here's a very familiar situation that shows up constantly on the backend. Some API returns data in pages, 50 or 100 records at a time, and you need to walk every page: sync them to your database, export them to a file, run a report. The endpoint gives you a cursor or a page number and you keep asking until there's nothing left. The way most of us write it the first time looks like this: async function getAllRecords () { const all = []; let cursor = 0 ; while ( cursor !== null ) { const { records , nextCursor } = await fetchPage ( cursor ); all . push (... records ); cursor = nextCursor ; } return all ; } const everything = await getAllRecords (); for ( const record of everything ) { process ( record ); } It works. At four hundred records it's fine. The trouble starts when the dataset grows, and it has three separate problems hiding in it. It holds the entire dataset in memory before you touch a single record. It's all or nothing: if page 380 fails, you've thrown away the 19,000 records you already fetched . And it's eager. You can't start processing record one until the very last page has landed , even if all you wanted was the first ten. There's a shape in JavaScript built for exactly this, and if you read the first two posts in this series you already have both halves of it. Two ideas you've already seen In the CSV post , we pulled rows out of a huge file one at a time with a generator, so the file never fully loaded into memory. Lazy. Pull-based. You ask for the next row, you get the next row, nothing more. In the async/await post , we saw that a generator can pause at a yield and resume later.A generator can hold its place across an asynchronous gap. Put those together. A generator that pulls data lazily, and can pause to await something between pulls. That's an async generator, and it's the natural tool for walking a paginated API. You pull records

How I Built My Indie AI Stack — A Practical Guide for 2026 A few months ago I hit a wall. I was bootstrapping a side project, burning through API credits way faster than my wallet could handle, and honestly questioning whether shipping a product as a solo dev in 2026 was even realistic anymore. The big-name providers were charging me an arm and a leg, and I kept reading about indie hackers who somehow made it work. So I went down a rabbit hole — tested dozens of models, tracked every dollar, and built what I now call my "indie AI stack." Let me show you exactly what I landed on, why it works, and how you can copy it. Why This Stack Exists (And Why I Almost Gave Up) Here's the thing nobody tells you when you're starting out: the default path — just throwing GPT-4o at everything — will quietly drain your runway. When you're an indie dev, every cent matters. I remember watching my first invoice roll in and doing actual math on whether I could sustain this for six months. The answer was no. So I started experimenting. I tested 184 different AI models (yes, really) through Global API, ran them against real workloads from my product, and started measuring not just quality but cost-per-useful-output. That's the metric that actually matters. The result? I landed on a stack that delivers 40-65% cost reduction versus just slamming GPT-4o on every request. Quality stayed comparable — sometimes better. Average latency sits at around 1.2 seconds with 320 tokens per second throughput. And the whole setup took me under 10 minutes. Let me walk you through it. The Models That Actually Made The Cut After weeks of testing, I narrowed my shortlist down to five models that form the backbone of my indie stack. Here's the pricing breakdown I'm working with today: DeepSeek V4 Flash — $0.27 input / $1.10 output, 128K context DeepSeek V4 Pro — $0.55 input / $2.20 output, 200K context Qwen3-32B — $0.30 input / $1.20 output, 32K context GLM-4 Plus — $0.20 input / $0.80 output, 128K context GPT

Reflecting on two major technical challenges from my backend engineering internship, focusing on fault tolerance, infrastructure, and distributed architectures. Introduction As I wrap up my HNG internship, I’ve been reflecting on the gap between code that "works on my machine" and code that survives in production. Here is a look at two tasks from Stage 9—one solo, one team-based—that completely changed how I approach backend engineering and infrastructure. The Individual Task: Background Job Scheduler What it was For my individual Stage 9 task, I built a distributed background job scheduler backed by PostgreSQL and a FastAPI backend, featuring a vanilla HTML/CSS/JS frontend. It manages async tasks (like a mock email sending queue) using a MinHeap priority queue, Directed Acyclic Graph (DAG) dependency resolution, a Dead-Letter Queue (DLQ), and a real-time Server-Sent Events (SSE) dashboard. The problem it was solving Heavy asynchronous tasks—like email generation or batch processing—cannot block the main API thread. The system needed to successfully queue, prioritize, retry on failure, and track every job entirely independently from the standard request-response cycle. How I approached it I built the core logic from the ground up: a MinHeap and an alternative Timing Wheel algorithm for scheduling, a worker engine featuring a 3-attempt backoff sequence (1s, 5s, 25s with jitter), a DAG dependency checker, and a starvation daemon to prevent tasks from hanging. Once the CRUD API and SSE streaming were hooked up, I containerized the entire application with Docker and wrote my deploy scripts. I thought I was done. What actually broke and how I fixed it The application code took hours. The deployment took a full day of non-stop debugging across multiple cloud providers. Oracle Cloud was out of capacity on every free tier shape, and GCP demanded upfront payment. I finally got a t3.micro running on AWS, but that’s when the real DevOps nightmare began: The SSL Chicken-and-Egg

I have been doing a lot of work lately tightening up my diagnostic suite: the mechanics, the workflow, the way it runs against target repos, the way it helps narrow a repair instead of letting everything turn into a fog machine. And because I work with Codex as my coding agent, I have also become very familiar with a specific kind of AI-agent behavior. The “I am helping so hard I am about to make this worse” behavior. If you work with coding agents, you probably know the vibe. You ask for one thing. The agent does that thing. Then it also adjusts a helper. Then it updates a fixture. Then it “notices” a nearby pattern. Then it starts explaining three other improvements you never asked for. And now you’re staring at the diff like: “Why are you in that file?” “I did not tell you to touch that.” “That was not the repair lane.” “Please stop being useful for one second.” I am not proud of how many times I have verbally threatened a language model. But here we are. The funny thing is, I am building Scarab partly because I already expect this kind of drift. I know that when an AI coding agent is given too much uncertainty, it tries to solve the uncertainty itself. Sometimes that is useful. Sometimes it is a raccoon with a soldering iron. The challenge is that while I am developing the diagnostic system, I cannot always use the diagnostic system to supervise itself. So there are moments where I have to manually hold the line. That means a lot of conversations with Codex that sound like: “Do not widen the patch.” “Do not change the diagnostic output to make the diagnostic pass.” “Do not fix the test by changing what the test means.” “Do not touch SDS mechanics while repairing the target repo.” “Stay in the target.” “Stay in the lane.” “Why are you like this?” Very normal. Very calm. Very professional. Then something changed At some point, after a lot of tightening, the workflow started to feel different. Scarab had enough of the diagnostic work under control that I could tell

Eight years ago (a geological epoch or two ago in Internet terms) Nicholas Jitkoff released itty.bitty.site - a website which could render whole websites just based on what was in the link, something like: itty.bitty.site?SOMEBASE64ENCODEDVALUE== et voilà! Free web-hosting if you could make it fit ;) At the time, I was rather obsessed with qr-codes thanks to developing QRGoPass and was working with aster plots a lot, so I developed an app that could fit into a qr-code! Today itty.bitty.site no longer exists so I can't do that any more... But I did make it 80% smaller without "cheating" and using modern CSS instead of d3.js ;)

The magnetic fields emitted by your headphones will need to be used a safe distance away from your CIDs.

Every time you use an online PDF converter, your file travels to a server somewhere — processed by a company you don't know, stored temporarily on hardware you don't control. For a random meme? Fine. For a payslip, a contract, or a medical document? That's a real privacy problem. What I built ConvertiZen is a document converter that runs 100% in the browser using WebAssembly and modern JS APIs. Your file never leaves your device. Ever. Verify it yourself: open DevTools > Network tab > run a conversion. Zero outgoing requests containing your file data. What it supports 40+ format pairs: PDF ↔ Word, JPG, PNG, Text, HTML, Markdown Excel ↔ CSV, JSON, HTML Images: JPG ↔ PNG ↔ WebP, GIF/BMP → PDF JSON ↔ CSV, Markdown ↔ HTML, XML → JSON How it works PDF.js for PDF parsing docx library for Word generation SheetJS for Excel/CSV Canvas API for image conversions Everything client-side — no server required Pricing 3 free conversions/day, no account needed. Beyond that: €0.69/conversion, €4.99 for a pack of 10, or €4.99/month for unlimited Premium. Feedback welcome What formats are missing? What would make you trust a browser-based tool over a server-based one? 👉 https://convertizen.netlify.app

The all-in-one workspace for serious exam aspirants. Discussion | Link

A backend engineering internship retrospective I'm not going to write one of those blog posts where everything goes smoothly and the developer learns a neat lesson at the end. That's not what happened. This is about two tasks from my internship that were genuinely hard — one individual, one team — and the honest version of how they went. Part 1 — Stage 3: Build Insighta Labs+ What it was Stage 3 was the biggest individual task I'd faced up to that point. The assignment: take the demographic intelligence platform from Stage 2 — filtering, sorting, pagination, natural language search, all of it — and turn it into something real users could actually log into. Three separate repos. One backend. GitHub OAuth with PKCE, JWT access and refresh tokens, role-based access control (admin vs analyst), a globally installable CLI tool, and a web portal with HTTP-only cookies and CSRF protection. Break anything from Stage 2 and it counts against you. The problem it was solving The platform had no auth at all. Anyone could hit any endpoint. No ownership, no access control, no sessions. Stage 3's job was to fix that — not just slap a login button on it, but build a system where the CLI and the web portal could both authenticate against the same backend through completely different flows, and still behave consistently. How I approached it — the actual build I started with the backend and worked outward. The first thing I had to figure out was that the CLI and web portal OAuth flows are fundamentally different, even though they both use GitHub. The web portal flow is what most people think of as OAuth — user clicks a button, gets redirected to GitHub, GitHub redirects back to the backend callback, backend sets HTTP-only cookies, done. The tokens never touch JavaScript because they live in cookies the browser sends automatically. The CLI flow is weirder. There's no browser session to set a cookie on, so I had to think about this differently. What I ended up doing: when you run insighta

Voice dictation that adapts to what’s on your screen Discussion | Link

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A supercharged terminal for Mac with built-in local AI Discussion | Link

I turned off Siri on the Mac years ago and never looked back. Similarly, I found Apple Intelligence so fruitless I never engage with it. But the new Siri AI coming to macOS 27 Golden Gate has at least got me slightly rethinking things. I'm still early in testing Siri AI, as I've only had […]

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Available on the App Store Discussion | Link

Give agents reliable access to 2,000+ APIs w/ durable state Discussion | Link