DEV Community: Muktadirul675

My package just got 162 downloads and I thought it was idle!

Muktadirul675 — Sun, 22 Feb 2026 16:22:40 +0000

I published a npm package called @lightrec/lr-ibcf about a month ago. I know this isn't something to write about, but I thought my package was just uploaded to npm and then nothing. I suddenly thought I should check the package and it got 162 downloads in 1 month!

Check the package on github!

Download it via:
npm install @lightrec/lr-ibcf

Happy Coding!

Developed My Portfolio with coder vibe!

Muktadirul675 — Mon, 02 Feb 2026 13:28:26 +0000

Hello folks!

This is my updated portfolio. I have it idle for about 3-4 months, and thought I should make some updates. So here it is. You are welcome to visit!

Portfolio:
https://muktadirul.vercel.app

Portfolio - MD Muktadirul Islam Mahi

Full Stack Developer skilled in Next.js, Django, Node.js & modern web technologies. I build fast, scalable, and user-focused web applications.

muktadirul.vercel.app

For JS devs: LightRec/lr-ibcf - Open-Source Item-Based Collaborative Filtering in JavaScript

Muktadirul675 — Sat, 31 Jan 2026 10:31:27 +0000

Hello folks! I know we all have seen sections like "Recommended For you", "You may like it" in various platforms. As a developer, I always thought how they made it. After some research I found out that these are made using Machine Learning methods via python.... Wait a bit!
What about the js developers who write their whole code via JS? I am a developer who uses JS a lot and implementing a full machine learning model for every model is tough. That's why I've made "LightRec" (https://lightrec.vercel.app), a platform for recommendation engines and I'll keep publishing new models here.

The first model is "lr-ibcf".

And, its quite easy to use.

1. Installation:

npm install @lightrec/lr-ibcf

2. Import and Initialize

import { RecEngine } from 'lightrec';

// Create a new recommendation engine
const engine = new RecEngine();

3. Pre-Train (Optional)

const interactions = [
    { userId: 'u1', itemId: 'i1', points: 5 },
    { userId: 'u1', itemId: 'i2', points: 3 },
    { userId: 'u2', itemId: 'i1', points: 4 },
]; // Your interaction data

engine.feed(interactions);

4. Train

const data = {itemId:'i1', userId:'u1', points:3}
engine.act(data.itemId, data.userId, data.points)

5. Recommend

const recommendations = engine.recommendForUser('u1', 5);
console.log(recommendations); // ['i3', 'i4', 'i5', ...]

And, that's it!
Only 5 lines of code and you get personalized recommendations!

But how this model works?
Its a Item Based Collaborative Filtering method. To know more stay tuned with me, and I'll write another article on how this works.

For now, you may check this github repo:
Github⭐

Have a visit to my portfolio!

Happy Coding!