Inside Token Tamer

Token Tamer started as a personal need to make hidden token costs visible. Under the brutalist surface it's a stack of deliberate choices—from Next.js and React to WebAssembly parsing and local embeddings—that keep every analysis private, fast, and explainable.

Illustration of a surgeon operating on a JSON payload

Technology highlights

Tree-sitter JSON parser

A WebAssembly build of Tree-sitter validates payloads locally and reports precise line and column errors, so messy JSON never leaves your browser.

Per-model tokenizers

LenML tokenizers mirror the official vocabularies for GPT-4o, Claude 3, Gemma 3, Qwen 3, and other modern models, so the counts you see match what APIs bill against.

Gemma 3n embeddings in-browser

Similar-key insights run entirely on-device. Compact Gemma 3n embeddings pair with a lightweight k-means pass to cluster redundant field names in milliseconds.

Analyzer worker + pruning engine

Token accounting runs inside a dedicated web worker that streams results back to the UI, letting you prune branches in milliseconds without blocking the main thread.

Performance-minded rendering

Large arrays render the first 200 children with expand-on-demand controls. That keeps the brutalist tree snappy even when you feed it megabyte-scale payloads.

Neo-brutalist design system

Thick borders, uppercase typography, and reusable components like BrutalistSelect and GuideLink keep every surface consistent—from the optimizer to the long-form guides.

The Optimization Imperative

This is why token efficiency matters:

  • Economic impact. Direct correlation to operating costs at scale.
  • Performance impact. Affects latency, throughput, and user experience.
  • Environmental impact. Every wasted token is wasted energy.
  • Competitive impact. In a world where energy becomes the bottleneck, efficiency becomes a strategic advantage.

When I built Token Tamer, I wanted to make these hidden costs visible. Because once you see them, you can’t unsee them. Every verbose field name, every redundant structure, every unnecessary nesting level—they all add up. And in the age of ubiquitous AI, what adds up eventually matters.

What’s ahead

You tell me. Drop the next capability you want in the contact form and help steer the roadmap.