Loodio – A Bathroom Privacy Device

Hacker News

Hi HN! A few years ago our company moved to a new office. That office has a insufferable bathroom in terms of privacy. You could hear a needle fall on the floor inside of it which made it very uncomfortable to use the bathroom. Many people ran the tap water or fake coughed to cover their “noises”, especially doing “no 2”. I got sick of not being able to be comfortable in the bathroom and hearing everyone’s toilet business so I decided to make a music player using a Raspberry Pi and soldered on a motion detector that started a white noise and music whenever you entered the bathroom to hide these sounds. A lot of people said they wanted one so I thought hey I should try to make this into a product. A lot of time and money was wasted hiring consultants until I 13 months ago said “enough of wasting money, I’ll just build it myself”. Today I launched a kickstarter and am selling 50 units. Link: https://www.kickstarter.com/projects/loodio/loodio-bathroom-... I know people in US have loud fans and such to cover for this, and a lot of people just play music on their phones but the problem with that is: when you manually play music in there you indicate you are going to do something embarrassing! How else are you solving this problem? I tried all existing solutions like the Sound Princess from Japan and other chinese crap on Amazon/Ali but they were not up to the task. I think there could be a market for Loodio. What do you think?

Visit product View on Hacker NewsApr 27, 2022

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

93%93% 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: exist, existing, ide · Missing: https docs, excited, just released

64%64% 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, using · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · 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 · 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 · Strong signals: arr · Missing: mrr, revenue, profit

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

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

Correct prediction on native model

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