I made a Telegram bot for summarizing and posting top HN stories

Hacker News

Hi HN, I made a Telegram bot that summarizes top Hacker News stories and posts them to a Telegram channel: https://t.me/hnbriefs . What it does: - Every 10 minute, the bot scrapes the Hacker News API for top stories. - It filters and posts stories with a score of 100+ - Each post includes a brief summary of the story. Why I built it: I found myself wishing for a summary of a story to decide whether I want to delve deeper and read the full article. Sometimes, a brief summary is all I need. You can find the source code on Github: https://github.com/johntad110/hnbriefs I'd love to hear your feedback and suggestions!

Visit product View on Hacker NewsJun 27, 2024

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1points

Did not reach leaderboard

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

42%42% predicted probability of success on Indie Hackers, 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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% 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 HuntUnlikely to reach the leaderboard · Strong signals: new, code · Missing: mac, agents, macos

19%19% 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

16%16% 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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