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Starsling, a social goal-tracking site. Applying for YCS12

Hacker News

Starsling, a social goal-tracking site. Applying for YCS12

Just wanted to tell everyone about our idea that we submitted. We want to remain transparent in our process. We have noticed a lot of people becoming interested in the quantified self with the release of such devices as the Jawbone UP, Nike Fuel Band, and the FitBit. We think there can be a better network for all of the people that currently use those devices and also people who want to manually track their progress. Starsling will be a social goal tracking site where you can see how your friends are doing on their goals, give them motivation, and challenge or join your friends in their goals. Create goals such as Meditate 7 times a week, or read 48 books in a year. Starsling will also allow you to track your goals with or without the aid of such monitoring devices like the Jawbone UP and display your data in a helpful way. If you have any feedback or thoughts please let us know! jeffreykam & gregsqueeb http://starsling.com/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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 · Missing: mac, agents, macos
66%66% 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
46%46% 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
40%40% predicted probability of success on AppSumo, 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
38%38% 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 · Missing: arr, mrr, revenue
16%16% 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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