GP

GP-HIIT: A simple, straightforward HIIT training app

Hacker News

GP-HIIT: A simple, straightforward HIIT training app

During my summer holidays, a visit to the pharmacy and stepping up on the scale, prompted me to do something concrete to keep my health (specifically, my weight) in check. As per my brother recommendation, I subscribed to https://www.freeletics.com/en/ and I can't recommend it enough. However, as a small excuse to learn a bit of Angular and meshing it with some LLM prompt engineering, I made a very basic, straightforward HIIT app that anyone can use. You can choose from three difficulty levels for your workouts which include a warm-up, a main workout session and a cooldown. I've also added some possible meal plans/pairing of nutritional meals that are typically combined with working out to watch out for your weight. This is _much_ simpler than the Freelectics workouts, it's not as personally tailored, but, it's free and it's for something that can potentially help people get in shape, so, I've decided to share it here. Feedback is welcome. :)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% 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.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
49%49% 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.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac · Missing: agents, macos, agent
29%29% 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
23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
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.

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