GitHub

What it does

TokenShrink compresses LLM prompts before they hit the API — stripping filler, hedges, and bloat without changing meaning. 100+ regex rules run in under 2ms. No API calls. No cost. Never touches code blocks or URLs. Works as CLI, MCP server, and browser extension. English + Turkish. Optional Groq upgrade for LLM-quality compression (14,400 free req/day). Opus 4 costs $15/M tokens — a heavy user spends ~$68/yr on input. TokenShrink cuts that in half. For teams of 10, that's $680/yr saved.

Compress LLM prompts 30-60% — CLI, Claude Desktop MCP, browser extension. Zero API calls. Zero cost. - GitHub - lhr-present/tokenshrink: Compress LLM prompts 30-60% — CLI, Claude Desktop MCP, browser extension. Zero API calls. Zero cost.

Cut your LLM token usage by 30–60% on every message. Zero API calls. Zero cost. Works everywhere. Before: "I was wondering if you could please help me understand how transformer neural networks work and why they have been so effective in the field of natural language processing, if you don't mind." → 47 tokens After: "Help me understand how transformer neural networks work and why they're so effective in NLP." → 18 tokens Saved: 29 tokens (62%) — instant, local, $0.00 Why this matters Claude Opus 4 costs $15 per million input tokens . If you send 500 words per message, 100 times a day, that's ~12,500 tokens → $0.19/day → $68/year . TokenShrink cuts that in half, automatically, with no…from github.com

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  Hey HN! Tokencost is a utility library for estimating LLM costs. There are hundreds of different models now, and they all have their own pricing schemes. It’s difficult to keep up with the pricing changes, and it’s even more difficult to estimate how much your prompts and completions will cost until you see the bill. Tokencost works by counting the number of tokens in prompt and completion messages and multiplying that number by the corresponding model cost. Under the hood, it’s really just a simple cost dictionary and some utility functions for getting the prices right. It also accounts for…

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  Hi HN, I was once given the advice: Don't waste expensive frontier model credits (GPT/Claude/etc.) on bulk work. Send the boring, repetitive, high-volume jobs to a smaller model, and save the expensive prompts for when you actually need frontier-level reasoning. I complained and told my manager that I shouldnt have to think about using certain models for certain coding tasks, and that one model should handle everything. Well, here we are anyway. If anyone needs a place to absolutely abuse an LLM with high-volume tasks, come beat ours up at https://yolo-auto.com. Here are…

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