Tldr.cafe – Instant Summaries of HN Posts

Hacker News

Hi, I'd like to share a side project tldr.cafe that gives you quick summaries of Hacker News posts. Just add tldr.cafe/ before any HN link, and you’ll get a neat digest of the content and discussion. For example: https://tldr.cafe/https://news.ycombinator.com/item?id=41491... https://tldr.cafe/https://news.ycombinator.com/item?id=41497... https://tldr.cafe/https://news.ycombinator.com/item?id=41502... It’s been super helpful for me when I want to catch up on posts without diving into every comment. Would love to hear your thoughts and feedback!

Visit product View on Hacker NewsSep 11, 2024

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

69%69% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

27%27% predicted probability of success on Product Hunt, 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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