I'm measuring energy usage with time during command execution

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Hello! This program called 'etime' is a tool I've been building to measure energy usage with the 'time' command. I've been interested in measuring the total amount of energy my desktop uses when running certain processes that are compute intensive - especially as it relates to execution time. [0] This project uses the API behind a smart plug [1] and the start and end times of a process to compute joules and watts used by that process. It overlays the 'time' command to collect 'user' and 'sys' time information too! Writing the program has been exciting explorations into shell built-in redirections and build setups for testing and releasing. [2] It's also been useful in solving my initial interests and hopes! [0]: https://o526.net/code/emporia-time/ [1]: https://www.emporiaenergy.com/emporia-smart-plug/ [2]: https://github.com/zimeg/emporia-time/

Visit product View on Hacker NewsJul 15, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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