Weekly summary of HN to help fight procrastination

Hacker News

Lately, I've realized I'm spending a lot of time on HN. I've also realized that my brain uses it as a some sort of "escape" route: whenever I'm dealing with something complicated, my brain defaults to Cmd+T + "news..". So I've decided that I need to stop continuously browsing HN. BUT, I still don't want to miss the relevant news, events and discussions. That's why I've built this very simple, statically generated summary of HN posts (by year, month and week): https://hn-summary.github.io/ I think it's also a good way to filter the noise and only focus on those relevant posts that had a considerable impact on their given week. I've also grouped the submissions on different (and arbitrary) groups that I consider relevant (for example: News, Dev Blogs, Scientific News, etc). Feel free to suggest changes/additions (it's all in a json file within the repo). I guess the only thing left is block news.ycombinator.com on my /etc/hosts :) [Source] https://github.com/hn-summary/hn-summary.github.io

Visit product View on Hacker NewsJan 2, 2022

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

61%61% 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 · Strong signals: month, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

26%26% 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

14%14% 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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