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Shorter Shower – Save water and time

Hacker News

Shorter Shower – Save water and time

TL;DR; This free mobile application helps you take a shorter shower by keeping you focused. This is done with routines comprised of activities (shampoo hair, wash body, etc). When the routine is started, the app will tell you which activity you should be doing. Download the application and let me know what you think. Always looking for feedback. Thanks!! Please up vote on Product Hunt: https://www.producthunt.com/posts/shorter-shower Longer version: My brothers and I took after my father in a lot of ways. One of the biggest was taking very long showers. When I moved to California, it was frowned upon to take a long shower due to it wasting water. I tried to take shorter showers but I kept zoning out. All of a sudden, 5-10 mins went by and I hadn't done anything yet. To solve this I tried setting timers, counting to myself, and a number of other ways. I realized the only way that I could consistently take a shorter shower is if something told me what I was supposed to be doing. That is when I created shorter shower. The app allows you to create activities (shampoo hair, wash body, etc) with an expected duration. You can combine the activities into multiple timers/routines. When you start the timer, it will change the volume of your phone to a predefined value so you can hear it over the shower noise. It will then audibly tell you what you should be doing when. It will stop the timer automatically when you are on the last activity and you turn off the shower. When the timer is done, the phone volume will be reset to its previous value. This app helped me go from 12 min showers to 5 min showers very quickly. A few notes about data. Activity and timer data is stored on the phone. I send water usage metrics to a server but do not include user information. This is mainly to answer the question, how much water are people saving / using each day. I also use GA to track user interactions.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
88%88% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, activity, using · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
21%21% 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
16%16% 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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