I made a solar-powered, ePaper photo frame

Hacker News

Looks like this year ePaper projects have been popping around[1], so this is mine. I made a photo frame with the intention of having a record of my family's history that can potentially outlive me. Knowing the challenges of long-lived digital projects, I consider this a moving target that will go through several iterations, but the first version showed encouraging results. I wonder if anyone else is interested in having something like that in their living rooms. More details: https://www.jamez.it/blog/2020/12/17/made-epaper-solar-powered-digital-photo-frame-call-solarpunk/ [1] https://news.ycombinator.com/item?id=22628348 https://news.ycombinator.com/item?id=22831323 and more recently https://news.ycombinator.com/item?id=25400702

Visit product View on Hacker NewsDec 17, 2020

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% 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.

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

33%33% 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.

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

12%12% predicted probability of success on Product Hunt, 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.

Correct prediction on native model

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