Simple Hacker News Client for Emacs

Hacker News

I wrote this small client to read hackernews from my favorite editor. It's currently using an external HTTP API [0] but I'm working on my own proxy [1]. I'd love to actually scrape and cache all the info I need using lisp. However, my time constraints are keeping me away of that goal. I could definitely use some help here! :) I'm also planning to add support for comments and posting new things. Any feedback is highly appreciated! Thank you! [0] http://api.ihackernews.com/ [1] https://github.com/clarete/rhn

Visit product View on Hacker NewsOct 18, 2013

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Actual performance

81points

27comments

Made the leaderboard

Launch Intel predictions

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

60%60% 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 HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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