os

osod.im (one sentence, one day - a one sentence journal)

Hacker News

osod.im (one sentence, one day - a one sentence journal)

I'm trying to get the motivation up to actually finish building out a "real" product, and I found myself needing a "win" to keep my momentum up. So, I hacked out a little one sentence journal app last night. I experimented with a few UI things (e.g.., the autosaving of the sentence for today and so on), so it was a good learning experience also. You can check it out at: http://osod.im

Visit product View on Hacker NewsJan 26, 2012

Actual performance

11points

6comments

Made the leaderboard

Launch Intel predictions

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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, 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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% 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

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

15%15% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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