TweetCatcher – Twitter Bookmarks on Steroids

Hacker News

Hey Everyone! It's Firat from Fluffzy. We're a small product studio, and I'm so happy to share TweetCatcher at here. TweetCatcher is a mobile app for saving, organizing and finding Tweets you love. It's a simple app that solves a frustrating problem. Couple weeks ago my Co-founder Ege was trying to show me a funny Tweet from his bookmarks. He scroll through dozens times, try to use twitter search. At the end, he hopelessly tried Google search. We hate to go through all of our bookmarks, when we want to find a tweet we love. And It wasn't the first time we were frustrated. We have couple clients projects at hand, but we had our weekends. So, we build TweetCatcher in our weekends. It's a simple app that let you save tweets, create folders and search all your bookmarks.

Visit product View on Hacker NewsAug 15, 2021

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3points

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · 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: google · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

37%37% 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: google · Missing: mobile apps, ios, personal

23%23% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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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