Pratik Sharma

Advanced CSS Grid Techniques for Overlapping and Layered Designs Here’s a quick breakdown: Why Overlapping and Layered Designs Matter in CSS Grid Ever stumbled upon a design that feels dynamic, rich, and just a little bit magical? Chances are, it used overlapping elements to create depth and interest. In the realm of CSS Grid, overlapping isn’t just a nice-to-have—it’s a game-changer. But before you roll your eyes thinking […] 🔗 Read it here: https://lnkd.in/gwA4abYK Published on Stack-Daily #Blog #Insights #ContentMarketing

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗥𝗲𝗮𝗰𝘁 𝗥𝗲𝗻𝗱𝗲𝗿𝗶𝗻𝗴 Rendering turns your React components into elements. The browser shows these elements on the screen. React re-renders a component when its state or props change. Two types of rendering exist: 1. Initial Rendering This happens when your app loads. React builds the component tree and puts it on the page. 2. Re-Rendering This happens when data changes. A component re-renders if: - State changes - Props change - A parent component re-renders - Context values change The Virtual DOM improves speed. React follows these steps: - State changes. - React makes a new Virtual DOM. - React compares the new one to the old one. - React updates only the changed parts in the real DOM. This comparison process is called Reconciliation. It prevents React from rebuilding your entire page. It only updates what is different. Avoid unnecessary re-renders to keep your app fast. Use these three tools: - React.memo: This stops a child component from re-rendering unless its props change. - useMemo: This stores a calculated value so React does not redo heavy math every time. - useCallback: This keeps a function from being recreated on every render. The rendering lifecycle follows this path: - Component mounts. - Initial render. - State or props change. - Re-render occurs. - Virtual DOM comparison. - DOM updates. Source: https://lnkd.in/g3ykGKJA

Build Your Own AI Chatbot Like ChatGPT — A Practical Guide with Code From a Solo Dev Who Actually Did It Hey, I’m Rajni — an AI developer who once spent 72 hours straight trying to get a language model to write a love poem in Hindi. It failed. But that failure taught me more than any tutorial ever could. Today, I’m walking you through how I built a working ChatGPT-like AI — not with billion-dollar GPUs, but with free tools, open-source models, and a lot of coffee. This isn’t theory. This is code that runs today. You don’t need GPT-4. Start with open-source LLMs that fit on a laptop. # Best free models \(2025\) - microsoft/DialoGPT-medium → Fast, conversational - google/flan-t5-large → Great for reasoning - meta-llama/Llama-3-8B-Instruct → Best quality \(needs 16GB VRAM\) I chose DialoGPT-medium — it’s lightweight, MIT-licensed, and perfect for chat. from transformers import AutoTokenizer, AutoModelForCausalLM model\_name = "microsoft/DialoGPT-medium" tokenizer = AutoTokenizer.from\_pretrained\(model\_name\) model = AutoModelForCausalLM.from\_pret https://lnkd.in/gNM8C_3f