4 ms·
Show HN: Tokensift, an open-sourced token-efficiency linter for LLM prompts
- Sky_Joy3 8d ago[flagged]
- ritenv 21d ago[dead]
- dsemakin 21d agoDoes it work also on multi modal LLMs?
- ritenv 21d agoIt does work partially. TokenSift can analyze the textual parts of prompts sent to multimodal models, but doesn’t calculate or optimize image/audio/video token usage. Non-text payload calculation is something I’d like to add.
- ritenv 20d agoHello, author here. Most prompts include personalizing with a user's data. These prompts grow, and there is quite a bit of extra content that can be removed. UUIDs, pretty-printed JSON, ISO timestamps, repeated instruction blocks, etc., and all of it tokenizes to cost a lot. tokensift is a linter for that. It tokenizes the prompt with a real encoder the specific model uses: exact BPE counts for OpenAI and a calibrated estimate for Claude with the measured error shown. Then, it runs a set of rules against it and reports where the tokens are going, along with the dollar cost per finding. Works as a library or a CLI, and runs in CI. Current state: 20 rules, OpenAI and Claude supported, no Gemini yet.
- useiris 20d ago[flagged]