TLDR (short) news website with my own algorithm

Hacker News

I'm actively working on a news shortening website. Trying to save you from clickbaits, save your time and save you from reading Bible-sized articles What I hate the most when reading news is reading Bible-size articles just to find the main point of the article. Usually journalists write a bunch of unnecessary information just to create 400+ long articles and position better on Google. Worst of all are those clickbait titles or titles that tell you a portion of information just to make you intrigue and make you click on the article. For me personally, it is very annoying and frustrating. I like to spend like 30 seconds tops on an article, I want to get the information as fast as possible and move on. So with my [ExcerptDaily]( https://excerptdaily.com/ ) I'm trying to: * Save people's time * Inform you as fast as possible * Give you the main point of an article in 5 sentences * Save you from clickbait or half clickbait titles I wrote my own algorithm and tried a few of them, now I'm focusing on my own algorithm and making it better. I focused on the US market and CNN only so far, it is my ground zero.

Visit product View on Hacker NewsJun 9, 2022

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Actual performance

3points

4comments

Did not reach leaderboard

Launch Intel predictions

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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: google, new, using · 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: io · Missing: https docs, excited, just released

57%57% 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: personal, google · Missing: mobile apps, ios, entrepreneurs

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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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