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SitzProbe.app – Spaced Repetition for Performances and Complex Tasks

Hacker News

SitzProbe.app – Spaced Repetition for Performances and Complex Tasks

Hi HN! I’m super excited to share with you my first completely self-developed app: SitzProbe[1]. This is an application that came from a simple (yet unfilled) need I had. When sitting down to practice music, I had trouble keeping track of all the different pieces I knew and which ones I have or haven’t played recently. But I wanted to make sure I kept practicing the old ones mixed in with the new, so ended up with a mess of lists and notes on my phone. I’ve also always been fascinated by the theories of spaced repetition[2], but they’ve always been geared toward very atomic bits of learning (languages, technical vocabulary, etc.). So I decided to combine the two into a slightly fancier todo list: one where your tasks will never be “done”, but if you do them well, you do them less often; and if you don’t do so well, it will prompt you to repeat them more often until you do them better. As far as I could find, this sort of thing wasn’t available, so I made one. Any feedback, comments, critiques, are appreciated. There are certainly bits of the app that aren’t finished (what software is ever complete?) but would also love suggestions on extra things I might consider adding in the future. [1]: https://sitzprobe.app/ [2]: https://en.wikipedia.org/wiki/Spaced_repetition

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
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.
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.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, tasks, notes · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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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