TweetScreenr – fetch external links from your Twitter feed

Hacker News

TweetScreenr ( https://tweetscreenr.com/ ) goes through your Twitter timeline (and your twitter lists) and fetches only those tweets with an external link. TweetScreenr grew out of my frustration with Twitter. I found myself relying on Twitter to follow a particular research community, but realized that I do not care about the actual tweets/threads - all I wanted was the link to the underlying paper/article. I also did not like how often I got sucked into reading an opinion thread (usually political), and I walk away 30 minutes later thinking to myself "do people really believe those things?". All I want out of Twitter is some sort of personalized hacker news, and this is my attempt at creating it. Apart from viewing the external links/articles on the TweetScreenr web app, you can optionally receive a daily/weekly email digest or read them on an RSS reader. The premium version ($1 a month) supports blocking specific domains and accounts, and also supports Twitter Lists.

Visit product View on Hacker NewsMar 29, 2023

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Actual performance

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Launch Intel predictions

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

73%73% 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.

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs

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

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

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% 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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