I made a minimal E-Ink clock with a Raspberry Pi

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I made this DIY E-Ink Display with a Raspberry Pi and a Web UI hosted on the pi to customize and update the display with different plugins. It's all open source if you want to build your plugins or contribute to the project. Currently supports 4 plugins, more coming soon: - image upload - clock w/ 4 faces - ai image from text prompts - daily newspaper front covers Components: - 7.3 inch 7-color Inky Impression by Pimoroni - Raspberry Pi Zero 2 W - Ikea picture frame - MicroSD card & Micro USB cable for power github: https://github.com/fatihak/InkyPi tutorial: https://youtu.be/L5PvQj1vfC4

Visit product View on Hacker NewsDec 31, 2024

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

82%82% 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: open source, io · Missing: https docs, excited, just released

59%59% 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: new, open · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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