AI Weekly Malaysia

An ngrok blog post by Annie Sexton walks through compression fundamentals—minification, run-length encoding, and the three organs of modern compressors (transforms, models, entropy coders)—to argue that compressors and LLMs are solving the same underlying problem: prediction. The piece uses interactive code examples to show how redundancy reduction maps to predictive modeling.

Why: If you build with LLMs, understanding that compression and language modeling share the same mathematical core gives you a mental model for why quantization, tokenization, and context-window tradeoffs behave the way they do. Worth reading before optimizing model deployment costs or choosing compression for model weights.