Show HN: LLM Attention Visualization

This article introduces an interactive visualization tool that demonstrates how transformer-based language models use attention mechanisms to selectively draw from previous tokens when generating text. The visualization reveals that models can accurately copy information (like addresses and dates) by identifying and focusing on relevant past tokens with high attention weights, and can even combine information from multiple sources to generate contextually appropriate words. The implementation uses a React app with Transformers.js and pre-generated prompts to make the visualization accessible in the browser.