Filter and Monitor Hacker News Based on Your Interests

Hacker News

HN is one of my main sources of information for various topics, but I sometimes miss really interesting posts if I'm not regularly checking the front page. That's why I built an automated HN news monitoring service that delivers relvant news straight to my inbox or RSS feed. It uses LLMs to extract, summarize, and classify the front page articles and analyzes the different perspectives in the comments. Things to fix: - adjustable timeframe - update comment perspectives more often

Visit product View on Hacker NewsMar 6, 2024

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Actual performance

6points

1comments

Did not reach leaderboard

Launch Intel predictions

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

62%62% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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 · Missing: mobile apps, ios, personal

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

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

25%25% 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

13%13% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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