However Briefly – RSS/News timeline app

Hacker News

I created However Briefly several years ago so I could have a personal news timeline. I wanted to be able to briefly catch up with my interests from my phone and avoid any doom-scrolling or rabbit hole adventures. I was inspired by the minimal style of Hacker News. I can quickly scan for stories that interest me and decide without images or clickbait if I want to pursue the link further. Curating my own list allows me to distance myself from negative feedback loops common to social media news sources. Indeed there are plenty of rss apps that provide similar functionality but I wanted something that required as little management as possible. I seeded the news sources from my personal list and then let llm take over. It's been fun watching what it comes up with and I'm excited to see what happens with more users. I'm interested seeing if people still value the less is more approach these days.

Visit product View on Hacker NewsFeb 27, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

87%87% 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: apps, user, new · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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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