NightOwl – Loginless Anonymous Social Network

Hacker News

NightOwl is a attempt to create a loginless social network of anonymous people sharing their thoughts. You don't need to login, yet you can post and follow other people. Your username and activity is preserved even after you close the website. All NightOwl users are anonymous by default. You can however choose to opt out of anonymity by setting a preferred username and your Name in your profile. Your feed will consist of randomly selected thoughts from users you follow as well as some thoughts from other random people. NightOwl is heavily inspired from NightChamber and initially, started out as a NightChamber clone. http://nightowl-arko.rhcloud.com/

Visit product View on Hacker NewsSep 8, 2015

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Actual performance

4points

Did not reach leaderboard

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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

65%65% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, activity · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on Indie Hackers, 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.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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