I Built a Token Compressor That Cuts LLM Context Size by 60%

<h1> Cut LLM prompt tokens on structured data — losslessly </h1> <p><em>A small, dependency-free tool for shrinking logs, JSON, and CSV in prompts — without dropping a single

<p>I've been working on a side project called <strong>SuperCompress</strong> — an intelligent prompt compression system for LLMs. The idea is simple: most tokens you send to an

<p>I run a lot of RAG pipelines. And for a while, I was doing what most people do: retrieve the top-k documents, concatenate them into the context, and ship the whole thing to the

<p>I've been building AI-powered tools for the past year, and one thing kept bugging me: I was wasting money on tokens.</p> <p>Not because my prompts were bad — but because they

<p>AI coding assistants are useful. They're also expensive if you're not paying attention. I was spending $120/month before I started tracking. Now I spend under $50 for the same

<p>GPT-5.4 shipped on March 5. Seven weeks later, on April 23, 2026, OpenAI released GPT-5.5 — and the pace alone tells you something about where this race is headed. This isn't

<p>How a networking student ended up writing Rust, beating industry-standard compression, and learning more about computers than any classroom taught me.</p> <p>The Problem That

<ol> <li> <p><strong>The problem</strong></p> <ul> <li>LLM coding agents on large repos burn tokens scanning files. Claude Code on an 829-file codebase consumed 45K tokens just