AI

AI Meter – Local token usage with energy and water estimates

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AI Meter – Local token usage with energy and water estimates

[Problem] I wanted to track how many tokens I am burning and it's estimated energy and water impact. [WHAT THIS IS] A local token usage tracker that works across different coding harnesses and estimates energy and water impact [MY EXPERIENCE] Tracking this really put things into perspective. My personal usage is close to 1 MWh in the past three months which I had no idea about. This does not include my AI usage at work which is probably 10x. [DEMO] If you are interested in tracking these numbers locally, try out: https://ai-meter.app - It reads historical data if the files are present. Works completely locally (except to check if there is an update for the widget) - No login. No analytics. No tracking. ---- [On estimates] AI Meter measures tokens from local AI coding-tool logs, then estimates: Electricity: tokens ÷ 1M × 0.39 kWh, calibrated from Oviedo et al.’s production-scale inference model. Direct cooling water: electricity ÷ 1.20 PUE × 0.45 L/IT-kWh, using LBNL’s U.S. data-center WUE scenario. These are adjustable estimates, not provider measurements. They exclude training, hardware manufacturing, electricity-generation water, and local-device energy. ---- Hope you find this interesting! Please feel free to give feedback, would love to make this better!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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