
Tokenscout
A design-token extractor for live, rendered websites
What it does
Extract design tokens from a live, rendered website perceptual color clustering (CIELAB ΔE76), type/spacing scales, assets, and motion. Zero-dependency core. - Atroci/tokenscout
Does the same job
all alternatives →- TATokenDagger – A tokenizer faster than OpenAI's Tiktoken2025 · github.com · ▲281
TokenDagger is a drop-in replacement for OpenAI’s Tiktoken (the tokenizer behind Llama 3, Mistral, GPT-3.*, etc.). It’s written in C++ 17 with thin Python bindings, keeps the exact same BPE vocab/special-token rules, and focuses on raw speed. I’m teaching myself LLM internals by re-implementing the stack from first principles. Profiling TikToken’s Python/Rust implementation showed a lot of time was spent doing regex matching. Most of my perf gains come from a) using a faster jit-compiled regex engine; and b) simplifying the algorithm to forego regex matching special tokens at all.…


- TCtextshader.com2022 · textshader.com · ▲260
TLDR: Click the link to see some cool visualizations, refresh a few times for random examples, and try editing some of the code yourself. This is a small weekend project of mine inspired by shadertoy.com It's a static single-page site hosted on GitHub pages so the website design is pretty barebones. I'm not a web developer so I mainly wanted to create a Cool Thing with my free time and not have to learn tons of front-end to do it. I'd love to hear what people think about it and please post or send me any shaders you make! You can use the link button in the bottom right to share. I hope that…
- CACodeBurn – Analyze Claude Code token usage by taskApr 2026 · github.com · ▲112
Built this after realizing I was spending ~$1400/week on Claude Code with almost no visibility into what was actually consuming tokens. Tools like ccusage give a cost breakdown per model and per day, but I wanted to understand usage at the task level. CodeBurn reads the JSONL session transcripts that Claude Code stores locally (~/.claude/projects/) and classifies each turn into 13 categories based on tool usage patterns (no LLM calls involved). One surprising result: about 56% of my spend was on conversation turns with no tool usage. Actual coding (edits/writes) was…
Design Token ExtractorApr 2026 · chromewebstore.google.comTurn any website into reusable design tokens instantly
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