I made myself a HN site in English and Chinese, so I can read more HN

Hacker News

It is a single static html page, I also provide jsonfeed[0] and rss[1], and I incidentally support Traditional Chinese[2], Japanese[3], and English only[4], but I don't think I currently have English users or Japanese users. I tried to get `og:image` at the same time as getting hacker news' post to display the image, and I used deepl's API for translation. If you happen to need this, feel free to use it! There are more hn topic subsite like Show HN[5], Ask HN[6], Best HN [7], HN Front[8] etc. [0]: https://hnnew.buzzing.cc/feed.json [1]: https://hnnew.buzzing.cc/feed.xml [2]: https://hnnew.buzzing.cc/zh-Hant/ [3]: https://hnnew.buzzing.cc/ja/ [4]: https://hnnew.buzzing.cc/en/ [5]: https://showhn.buzzing.cc/ [6]: https://askhn.buzzing.cc/ [7]: https://hn.buzzing.cc/ [8]: https://hnfront.buzzing.cc/

Visit product View on Hacker NewsNov 9, 2022

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3points

Did not reach leaderboard

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

58%58% 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.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

52%52% 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

42%42% 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 · Strong signals: users · Missing: plus, platform, intuitive

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

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

30%30% 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

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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