Author: Arunangshu Das

Large Language Models (LLMs) have become incredibly powerful, but their massive size makes them challenging to deploy efficiently. That’s where LLM distillation comes in—shrinking these models while retaining their intelligence. The goal is to create a lighter, faster, and more cost-effective version of the model without sacrificing too much performance. If you’re looking to distill an LLM effectively, here are seven practical tips to ensure the process is smooth and impactful. 1. Focus on Task-Specific Knowledge Retention Not all knowledge in an LLM is equally useful for your application. If you’re distilling an LLM for code generation, for example, you…

JavaScript is a vast language, and no matter how much experience you have, there’s always something new to learn. Whether you’re preparing for an interview or just want to challenge yourself, testing your JavaScript knowledge is a great way to identify gaps and improve. Below are seven carefully crafted JavaScript questions—some tricky, some fundamental—that will help you gauge your expertise. Take a moment to think through each one before checking the explanation! What will be logged in the console? console.log(1 + “2” + “2”); console.log(1 + +”2″ + “2”); console.log(1 + -“1” + “2”); console.log(+”1″ + “1” + “2”); console.log(“A”…

A few years ago, I had the chance to work with a Principal Software Engineer who seemed to have a superpower: they always got things done—without looking stressed. Deadlines? Met. Code quality? Excellent. Meetings? Efficient. Meanwhile, the rest of us were scrambling to keep up. So, I did what any ambitious developer would do—I paid attention. I took notes. And eventually, I “stole” some of their best productivity hacks. Here are seven strategies that changed the way I work. 1. Start Your Day With a “Big 3” List Most people begin their workday by checking emails or Slack, which is…

If you’ve been working with Node.js for a while, you know that package.json is the heart of your project. It keeps track of dependencies, scripts, metadata, and more. But despite its importance, developers often make mistakes that can lead to broken builds, security issues, or just plain confusion. Let’s go over seven common mistakes in package.json configuration—and how to avoid them. 1. Forgetting to Set private: true for Internal Projects If you’re working on a private project that should never be published to npm, but you forget to set “private”: true, you risk accidentally publishing it with npm publish. The…

Node.js has become a must-know technology for developers working with JavaScript, backend services, or full-stack development. Whether you’re preparing for an interview or just sharpening your skills, knowing the most commonly asked Node.js questions can give you an edge. 1. What is Node.js? Node.js is a runtime environment that allows JavaScript to run outside the browser, built on Chrome’s V8 engine. It’s event-driven, non-blocking, and great for scalable network applications. 2. How does Node.js handle asynchronous operations? Node.js uses an event-driven, non-blocking I/O model through callbacks, Promises, and async/await. It relies on the libuv library for handling asynchronous tasks like…

JSON Web Tokens (JWTs) are often praised for their simplicity and scalability in stateless authentication. They allow clients to authenticate once and use the token for subsequent requests without needing to hit the database again. Sounds great, right? Well, not always. While JWTs have their place, they might not be the best choice in some situations. Let’s break down five reasons why JWTs could be a poor fit for your authentication needs. 1. Revocation is a Nightmare One of the biggest challenges with JWTs is that once a token is issued, you can’t easily revoke it until it expires. Unlike…

How big platforms like Twitter, Instagram, or GitHub handle username availability in real-time? When you sign up and type in your desired username, it instantly tells you whether it’s available or already taken. It looks simple, but behind the scenes, there’s a lot of engineering at play. 1. The Basics: Where Are Usernames Stored? Usernames are typically stored in a database, often indexed for fast lookups. The common choices are: Relational Databases (SQL-based): MySQL, PostgreSQLNoSQL Databases: MongoDB, DynamoDBKey-Value Stores: Redis (for caching purposes) In a typical scenario, when a user enters a username during registration, the system queries the database…

Fine-tuning AI models isn’t just about feeding them more data and hoping for the best. It’s an art—a process that requires precision, experimentation, and a deep understanding of how models learn. Whether you’re working with large language models (LLMs), vision models, or even small-scale ML projects, fine-tuning can mean the difference between a mediocre model and one that truly excels. Here are seven essential tips to help you fine-tune AI models effectively. 1. Start with a Strong Baseline Model Before jumping into fine-tuning, pick a strong pre-trained model. Large-scale models like GPT, BERT, or ResNet have already been trained on…

Fine-tuning AI models is both an art and a science. Whether you’re working with large language models, computer vision networks, or any other deep learning architecture, getting the best performance requires strategic tweaking. It’s easy to fall into the trap of either overfitting or underutilizing your data, and that’s where best practices come into play. 1. Start with a Strong Pretrained Model Why reinvent the wheel? Pretrained models like GPT, BERT, ResNet, and others already have millions (or even billions) of parameters trained on vast datasets. Instead of training from scratch, use a pretrained model that aligns with your task.…

Artificial intelligence (AI) has come a long way from its early rule-based systems to today’s deep learning marvels. At the heart of AI’s adaptability is fine-tuning—the process of taking a pre-trained model and tweaking it to perform better on specific tasks. Whether it’s enhancing chatbot responses, improving medical diagnoses, or refining image recognition, fine-tuning allows AI to become more precise, efficient, and context-aware. But what exactly happens behind the scenes? What is Fine-Tuning? Think of AI models like a newly trained chef. A general AI model learns from massive amounts of data, just like a chef going through culinary school…