New subreddit: Interesting content unearthed from HN

Hacker News

There is a lot of very interesting content on Hacker News which is invisible because it is old. In fact, I'd say that most interesting HN content is out of sight, just because there are only 30 stories on the front page. Our new moderator dang pointed this out in a recent comment (https://news.ycombinator.com/item?id=7494464): "I have written software ... (which would be) ... a neat way for users to find old threads. HN's archives are rich." To serve us till such time as the software which dang mentions becomes available to us, I created a new subreddit for discovering and sharing great content (comments and discussion threads) from the rich HN archive: http://www.reddit.com/r/bestofhn/ I have seeded the subreddit with a few discussions from the past which I found interesting. Do take a look!

Visit product View on Hacker NewsApr 1, 2014

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

74%74% 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, new · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

32%32% 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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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