I waste my time extracting stuff every week from the Internet

Hacker News

I started a few weeks ago (6 to be exact) a curation process. To make it short, I selected 150 feeds of news that I think are relevant, including HN, download new items on a regular basis, pre-filter them using light llms to filter out things that I'm not interested in (like billionaire drama, politics, recruitment, fundraising, astrophysics, commercial software ...) and filter them manually using a tinder-like application for news, with a few internal criterias: like open source software, human written inspiring text, dislike pricing buttons... So basically, I got into a process of swiping 2500 or so times a week about content, before diving even more in the one that interested me at first glance, which means I've eaten a lot of my time for little to no value. Any genius (or stupid) ideas on how to do better? I'd like to continue, but as it is now, it's too much time consuming and I'll get bored soon ... Of course I could automate the selection with LLMs but that's not the point, I like human-picked stuff (although I may benefit from auto-filtering generated content if I knew how to). Thanks :)

Visit product View on Hacker NewsJul 29, 2025

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4points

Did not reach leaderboard

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

85%85% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, open · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

67%67% 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 · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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.

Incorrect prediction on native model

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