My

MyYogaFlow – Online Yoga Instructor

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MyYogaFlow – Online Yoga Instructor

Hi fellow hackers :) I am an indie hacker based in Switzerland who has been tinkering around with many side projects. Most of the things I developed were for myself or just to get familiar with a specific technology or piece of software. I never really deployed anything so far. Not too long ago, my SO and me bought a deck of cards with different yoga poses on them. Since we both work from our office chairs more than 8 hours a day, we wanted to do something for our body and this is where the yoga idea originated from. After the first 10 minutes of reading through the leaflet and another 30 minutes of choosing the poses and putting them in a somewhat meaningful order, we finally started with our own yoga session. It was clear to me: this is not something my SO wants to do every day. Doing the same order of poses every day wasn't an appealing alternative either. Away with the cards and on the lookout for an alternative again. Hence I started developing an application for us, that automatically generates a yoga session. It shows the poses one by one and guides you through the session. Each session is unique, which makes it more interesting over the days. The app even lets you create your custom flow that you select or it chooses among poses that fit your experience level just right. I have recently launched this application and made it publicly available. It is working (in most of the cases) and we are pretty happy about how it turned out. We used it many times in our lunch breaks or after a stressful day to calm down. I would be more than happy to get some feedback on it and some pointers on what to improve or focus on next.

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Indie HackersFits the IH revenue-focused audience · Strong signals: 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
53%53% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
33%33% 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
14%14% 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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