Mo

Move Frequently – Stay active with a 1-min exercise every 45 minutes

Hacker News

Move Frequently – Stay active with a 1-min exercise every 45 minutes

My buddy Miguel and I really like taking care of our health. So we eat healthy, we lift weights and all of that. However, last week we learned no matter how much you exercise, sitting for long periods of time is a risk factor for early death. And we also love building products, so we do spend a loooot of time in front of a computer. At first, we just wanted to build a tool for ourselves that reminded us to do a couple of exercises to break our sedentary work habits. But then we thought it would be much more fun if more people could join us and we could all take a break together to do a mobility/strength exercise every now and then. So we launched https://movefrequently.com/ : a world-wide initiative to perform a 1-minute mobility and strength exercise every 45 minutes. The tool has a universal timer so that everyone in the world using it will be presented with a simple exercise they can do in front of their computers. We hacked this on Friday, but plan to give it some love and add features if people like it. Check it out and let us know what you think. Source code can be found on https://github.com/MoveFrequently/movefrequently BTW, love Show HN!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% 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: computer, using, code · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
18%18% 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.

Incorrect prediction on native model

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