Custom HN RSS Feeds

Hacker News

Custom HN RSS Feeds

So I'm building a new Hacker News iPad application and the biggest challenge, personally, was to get the number of comments, points, submission time etc., from the frontpage, latest submissions and highest rated pages. Unfortunately the BigRSS wasn't much help, nor were the numerous APIs - so I made my own version, albeit not RSS spec compliant. Any suggestions for a more compliant feed are welcome! http://thequeue.org/api/frontpage.xml (updated every 4 minutes) http://thequeue.org/api/new.xml (updated every 6 minutes) http://thequeue.org/api/best.xml (updated every 10 minutes) 2 reasons for these odd update times: I do not want to stress ycombinator's servers. 2. dreamhost has odd CRON job times - basically nothing under a 4 minute interval is allowed. One of the key advantage of those feeds is the ability to preview the submissions before reading them. Feedback is always appreciated

Visit product View on Hacker NewsFeb 12, 2012

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Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

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

84%84% 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.

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

45%45% 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, apis · Missing: mac, agents, macos

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

28%28% 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

15%15% 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.

Correct prediction on native model

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