Pressn't – a site where you can only have a single post

Hacker News

Today's internet is filled with dopamine wells of content. I wanted to steer away from that and foster meaningful writing. So I made a site where you can only have a single post. The intention is to encourage thoughtful posts like the blogs we all love here at HN (Paul Graham's, fasterthanlime, Bartosz Ciechanowski's, etc). For now posts are only markdown, but I intend to make some markdown extensions to make posts more dynamic.

Visit product View on Hacker NewsDec 29, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos

83%83% 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.

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

53%53% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% 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

19%19% 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.

Correct prediction on native model

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