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Trckstr an app that converts revision notes into audio

Hacker News

Trckstr an app that converts revision notes into audio

Hi, I'm 18 still studying. I began recording my revision notes and listening to them on the bus and train repeatedly. I realized that I learn well by listening. I began sending these recordings to my friends and soon realized that this would probably be useful to all students. So here it is: http://trckstr.com PS - Please tell me what you think about the concept.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: recordings, notes · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, 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
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
28%28% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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