Compression and LLMs Solve the Same Core Problem
Original: Compression is prediction
Why This Matters
Bridging compression theory and LLM mechanics helps developers build intuition for model efficiency and optimization.
ngrok's Developer Educator Annie Sexton published a technical deep-dive on August 11, 2026, explaining that data compression algorithms and large language models are fundamentally solving the same problem: prediction through redundancy.
In a 3,739-word blog post on ngrok's engineering blog, Developer Educator Annie Sexton draws a detailed parallel between classical data compression techniques and how large language models (LLMs) operate at their core.
The article begins with foundational compression concepts, distinguishing between minification—which removes human-readable syntax not needed by machines—and 'true' compression, which exploits data redundancy. Using run-length encoding as an introductory example, Sexton demonstrates how a 28-character string ('AAAAAAAAABBBBCCDAAADDDDDDDDD') can be reduced to 12 characters ('A9B4C2D1A3D9'), shrinking from 224 bits to 96 bits—a 57% reduction.
The post is positioned alongside related ngrok blog content on quantization and prompt caching, signaling a broader editorial focus on explaining LLM efficiency techniques to developers. Sexton's central thesis—that compressors and LLMs are 'at their core, trying to solve the exact same problem'—serves as the hook for a deeper technical exploration of prediction as the unifying principle behind both fields. The post is part of ngrok's ongoing developer education efforts, with Sexton drawing on her background at PaaS companies including Heroku, Render, and Fly.io.