HackSnack – A Personalized Hacker Newsletter

Hacker News

Hi HN! I've noticed several "HN, but for X" posts over the last few weeks. I believe HN already covers almost every interesting topic, it's just a matter of surfacing the relevant content. That's why we built HackSnack to let everbody get their own personalized HN newsletter with relevant topics. We use LLMs to extract, summarize, and classify each HN article based on user-defined preferences. We also analyze the comments of each post and show the different perspectives and their frequency. Give it a try and let me know what you think!

Visit product View on Hacker NewsOct 28, 2023

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Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

78%78% 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.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

34%34% 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

33%33% 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

12%12% 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.

Correct prediction on native model

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