Po

Ponder – the best articles and essays on the internet

Hacker News

Ponder – the best articles and essays on the internet

Hello HN, I love to read articles, but I have trouble finding the bangers that really make you think. I want to be able to organize a huge database of articles and filter it by category then order it by quality to find the best ones worth my time. There's iMDb for film, Goodreads for books, AOTY for albums but nothing for articles and essays. That was the idea, and I've got it up and running. Where it's at honestly: ~500 articles cataloged, a small handful of active users, and very few ratings yet. It's early. I'm a solo builder and I'm hoping to find other people who find the website useful and have the same passion that I do for good longform. It is a user-fed database, so it needs a few more people before it will start to thrive. What I'd love feedback on: -Yes, I used AI to generate the website code. Is this a turn-off? Is it obvious? -Would you use a website like this to keep a log or find good reads? -Are there any articles that you've read that you still think about?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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: user, code · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% 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: users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder, 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 · Strong signals: active · Missing: arr, mrr, revenue
17%17% 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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