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SenseThing, sensor logging and remote monitoring for Linux

Hacker News

SenseThing, sensor logging and remote monitoring for Linux

SenseThing provides simple tools for sensors on Linux systems. Its key features are: - Continuous logging of system temperature, voltage, power, and clock speed data. - Remote monitoring of the same data. I upgraded my PC recently, and wanted to run some tests to make sure everything was running smoothly. Initially, I was running a bash script that called lm-sensors every couple of seconds while some benchmark was running. That was problematic for a few reasons: 1. lm-sensors's output is a bit annoying to turn into a machine-friendly CSV format. 2. Nvidia GPUs don't expose their sensor data in a way that's supported by lm-sensors, so I actually had to invoke nvidia-smi in addition to lm-sensors to get all the data I needed. 3. Calling "sleep" periodically inside a loop resulted in inconsistent time intervals between measurements. These problems resulted in me writing a very early version of SenseThing. The first iteration just took sensor readings from sysfs and nvidia-smi, and dumped them into a CSV file. Then, about a week later, I was setting up a little server that's now living under my couch. This isn't a machine I can readily access, and I'd rather not set up an SSH session every time I want to check on it. So, I decided to add a simple HTTP server to SenseThing that lets me check the server's temperature sensors from any other device on my LAN. With that out of the way, I decided I might as well tidy it up so that other people might want to use. So, here it is!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac · Missing: agents, macos, agent
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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