Raspberry Pi (or any Linux machine) monitor app

Hacker News

I made a system monitor app for new linux users. When I started playing around with raspberry pi, I always wonder why things like htop don't show cpu frequency (really, for me (and maybe a lot of people from windows) not seeing the frequency is super uncomfortable) So here we are, a monitor app that will display the cpu frequency, temperature and more! Some features: - Zero setup - Nothing is needed to install on the raspberry pi Prerelease download link: https://cloud.pegasis.site/s/HoXZEs7wLecMrsZ Github: https://github.com/PegasisForever/raspi_monitor_app This is my first post on hacker news :)

Visit product View on Hacker NewsJun 30, 2020

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4points

Did not reach leaderboard

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, user, new · Missing: agents, macos, agent

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

34%34% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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